{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XnwFMeytUoI4",
        "outputId": "3bcfc879-d5c9-4f1f-fd35-a5db2971cf91"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install python-igraph"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NsEAo2i6M9Hm",
        "outputId": "5c8de2cb-dd7a-436b-978a-b406c4152f45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting python-igraph\n",
            "  Downloading python_igraph-0.11.9-py3-none-any.whl.metadata (3.1 kB)\n",
            "Collecting igraph==0.11.9 (from python-igraph)\n",
            "  Downloading igraph-0.11.9-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)\n",
            "Collecting texttable>=1.6.2 (from igraph==0.11.9->python-igraph)\n",
            "  Downloading texttable-1.7.0-py2.py3-none-any.whl.metadata (9.8 kB)\n",
            "Downloading python_igraph-0.11.9-py3-none-any.whl (9.2 kB)\n",
            "Downloading igraph-0.11.9-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.4 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.4/4.4 MB\u001b[0m \u001b[31m43.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading texttable-1.7.0-py2.py3-none-any.whl (10 kB)\n",
            "Installing collected packages: texttable, igraph, python-igraph\n",
            "Successfully installed igraph-0.11.9 python-igraph-0.11.9 texttable-1.7.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import zipfile\n",
        "from itertools import compress\n",
        "import igraph as ig\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import display\n",
        "from matplotlib.patches import Patch\n",
        "from matplotlib.lines import Line2D\n",
        "from scipy.spatial.distance import pdist, squareform"
      ],
      "metadata": {
        "id": "9gxoK68pNFKN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# General upload of data\n",
        "\n",
        "# --- Function to extract a ZIP file to a target directory ---\n",
        "def extract_zip(zip_file_path, extract_to_dir):\n",
        "    if os.path.exists(zip_file_path):\n",
        "        with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
        "            zip_ref.extractall(extract_to_dir)\n",
        "        print(f\"✅ Extracted: {zip_file_path} → {extract_to_dir}\")\n",
        "    else:\n",
        "        print(f\"❌ File not found: {zip_file_path}\")\n",
        "\n",
        "# --- Function to get sorted list of Excel files containing \"Actor\" in filename ---\n",
        "def get_filtered_files(directory):\n",
        "    files = os.listdir(directory)\n",
        "    filtered = list(compress(files, pd.Series(files).str.contains(\"Actor\")))\n",
        "    return sorted(filtered)\n",
        "\n",
        "# --- Setup directories and ZIP paths ---\n",
        "base_dir = '/content'\n",
        "savognin_dir = os.path.join(base_dir, 'Savognin')\n",
        "sedrun_dir = os.path.join(base_dir, 'Sedrun')\n",
        "\n",
        "savognin_base_dir = '/content/Savognin' #important to define\n",
        "sedrun_base_dir = '/content/Sedrun'\n",
        "\n",
        "os.makedirs(savognin_dir, exist_ok=True)\n",
        "os.makedirs(sedrun_dir, exist_ok=True)\n",
        "\n",
        "zip_paths = [\n",
        "    ('/content/drive/MyDrive/Data Paper /Savognin_final.zip', savognin_dir),\n",
        "    ('/content/drive/MyDrive/Data Paper /Sedrun_final.zip', sedrun_dir)\n",
        "]\n",
        "\n",
        "# --- Extract ZIPs ---\n",
        "for zip_file, target_dir in zip_paths:\n",
        "    extract_zip(zip_file, target_dir)\n",
        "\n",
        "# --- List and filter Excel files ---\n",
        "list_cleaned_savognin = get_filtered_files(savognin_dir)\n",
        "list_cleaned_sedrun = get_filtered_files(sedrun_dir)\n",
        "\n",
        "# --- Print results ---\n",
        "print(\"📂 Savognin files:\", list_cleaned_savognin)\n",
        "print(\"📂 Sedrun files:\", list_cleaned_sedrun)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZMj0d0noFYMv",
        "outputId": "81a4501f-88df-41b5-d421-7b022a2addec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Extracted: /content/drive/MyDrive/Data Paper /Savognin_final.zip → /content/Savognin\n",
            "✅ Extracted: /content/drive/MyDrive/Data Paper /Sedrun_final.zip → /content/Sedrun\n",
            "📂 Savognin files: ['Actor 1.xlsx', 'Actor 2.xlsx', 'Actor 3a.xlsx', 'Actor 3b.xlsx', 'Actor 3c.xlsx', 'Actor 4.xlsx', 'Actor 6.xlsx', 'Actor 8.xlsx']\n",
            "📂 Sedrun files: ['Actor 1.xlsx', 'Actor 3.xlsx', 'Actor 4.xlsx', 'Actor 5.xlsx']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check each Dataframe by looping trough list_cleaned_savognin and list_cleaned_sedrun\n",
        "\n",
        "def preview_dataframes(file_list, base_dir, label):\n",
        "    for file in file_list:\n",
        "        file_path = os.path.join(base_dir, file)\n",
        "        try:\n",
        "            df = pd.read_excel(file_path)\n",
        "            actor_name = os.path.splitext(file)[0].strip().title()\n",
        "            print(f\"\\n📄 Full DataFrame for {label} Actor: {actor_name}\")\n",
        "            display(df)\n",
        "            print(\"-\" * 60)\n",
        "        except Exception as e:\n",
        "            print(f\"❌ Error displaying {file}: {e}\")\n",
        "\n",
        "# Preview all DataFrames\n",
        "preview_dataframes(list_cleaned_savognin, '/content/Savognin', 'Savognin')\n",
        "preview_dataframes(list_cleaned_sedrun, '/content/Sedrun', 'Sedrun')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gPUZBT9WF24P",
        "outputId": "e1e80413-2cda-4e3b-9960-ad461e41f962"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "📄 Full DataFrame for Savognin Actor: Actor 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Actor 1  Biodiversity (before process) Biodiversity (after process)   \\\n",
              "0  Actor 1                            5.0                             5   \n",
              "1  Actor 2                            NaN                           NA    \n",
              "2  Actor 3                            NaN                           NA    \n",
              "3  Actor 4                            NaN                           NA    \n",
              "4  Actor 5                            NaN                           NA    \n",
              "5  Actor 6                            NaN                           NA    \n",
              "6  Actor 7                            NaN                           NA    \n",
              "7  Actor 8                            NaN                           NA    \n",
              "\n",
              "   Landscape (before process) Landscape (after process)   \\\n",
              "0                         1.0                          1   \n",
              "1                         NaN                        NA    \n",
              "2                         NaN                        NA    \n",
              "3                         NaN                        NA    \n",
              "4                         NaN                        NA    \n",
              "5                         NaN                        NA    \n",
              "6                         NaN                        NA    \n",
              "7                         NaN                        NA    \n",
              "\n",
              "   Agriculture (before process) Agriculture (after process)   \\\n",
              "0                           5.0                            5   \n",
              "1                           NaN                          NA    \n",
              "2                           NaN                          NA    \n",
              "3                           NaN                          NA    \n",
              "4                           NaN                          NA    \n",
              "5                           NaN                          NA    \n",
              "6                           NaN                          NA    \n",
              "7                           NaN                          NA    \n",
              "\n",
              "   Hydropower (before process) Hydropower (after process)   \\\n",
              "0                          5.0                           5   \n",
              "1                          NaN                         NA    \n",
              "2                          NaN                         NA    \n",
              "3                          NaN                         NA    \n",
              "4                          NaN                         NA    \n",
              "5                          NaN                         NA    \n",
              "6                          NaN                         NA    \n",
              "7                          NaN                         NA    \n",
              "\n",
              "   Energy security (before process) Energy security (after process)   \\\n",
              "0                               5.0                                5   \n",
              "1                               NaN                              NA    \n",
              "2                               NaN                              NA    \n",
              "3                               NaN                              NA    \n",
              "4                               NaN                              NA    \n",
              "5                               NaN                              NA    \n",
              "6                               NaN                              NA    \n",
              "7                               NaN                              NA    \n",
              "\n",
              "   Finances (before process) Finances (after process)   \\\n",
              "0                        5.0                         5   \n",
              "1                        NaN                       NA    \n",
              "2                        NaN                       NA    \n",
              "3                        NaN                       NA    \n",
              "4                        NaN                       NA    \n",
              "5                        NaN                       NA    \n",
              "6                        NaN                       NA    \n",
              "7                        NaN                       NA    \n",
              "\n",
              "   Employment (before process) Employment (after process)   \\\n",
              "0                          5.0                           5   \n",
              "1                          NaN                         NA    \n",
              "2                          NaN                         NA    \n",
              "3                          NaN                         NA    \n",
              "4                          NaN                         NA    \n",
              "5                          NaN                         NA    \n",
              "6                          NaN                         NA    \n",
              "7                          NaN                         NA    \n",
              "\n",
              "   Status Quo of power relations (before process)  \\\n",
              "0                                             NaN   \n",
              "1                                             NaN   \n",
              "2                                             NaN   \n",
              "3                                             NaN   \n",
              "4                                             NaN   \n",
              "5                                             NaN   \n",
              "6                                             NaN   \n",
              "7                                             NaN   \n",
              "\n",
              "  Status Quo of power relations (after process)   \n",
              "0                                            NA   \n",
              "1                                            NA   \n",
              "2                                            NA   \n",
              "3                                            NA   \n",
              "4                                            NA   \n",
              "5                                            NA   \n",
              "6                                            NA   \n",
              "7                                            NA   "
            ],
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actor 1</th>\n",
              "      <th>Biodiversity (before process)</th>\n",
              "      <th>Biodiversity (after process)</th>\n",
              "      <th>Landscape (before process)</th>\n",
              "      <th>Landscape (after process)</th>\n",
              "      <th>Agriculture (before process)</th>\n",
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              "      <th>Energy security (after process)</th>\n",
              "      <th>Finances (before process)</th>\n",
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              "      <th>Employment (before process)</th>\n",
              "      <th>Employment (after process)</th>\n",
              "      <th>Status Quo of power relations (before process)</th>\n",
              "      <th>Status Quo of power relations (after process)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Actor 1</td>\n",
              "      <td>5.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>Actor 2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Actor 3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Actor 4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Actor 5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Actor 6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Actor 7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
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              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
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              "        const element = document.querySelector('#df-da2b4748-f2b1-4978-bcd1-df8fa54c1119');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "summary": "{\n  \"name\": \"preview_dataframes(list_cleaned_sedrun, '/content/Sedrun', 'Sedrun')\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Actor 1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Actor 2\",\n          \"Actor 6\",\n          \"Actor 1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Biodiversity (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 5.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Biodiversity (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": 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\"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Finances (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Employment (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 5.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Employment (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------------------------------------------\n",
            "\n",
            "📄 Full DataFrame for Savognin Actor: Actor 2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Actor 2  Biodiversity (before process) Biodiversity (after process)   \\\n",
              "0  Actor 1                            NaN                           NA    \n",
              "1  Actor 2                            1.0                             1   \n",
              "2  Actor 3                            NaN                           NA    \n",
              "3  Actor 4                            NaN                           NA    \n",
              "4  Actor 5                            NaN                           NA    \n",
              "5  Actor 6                            NaN                           NA    \n",
              "6  Actor 7                            NaN                           NA    \n",
              "7  Actor 8                            NaN                           NA    \n",
              "\n",
              "   Landscape (before process) Landscape (after process)   \\\n",
              "0                         1.0                          1   \n",
              "1                         1.0                          1   \n",
              "2                         NaN                        NA    \n",
              "3                         NaN                        NA    \n",
              "4                         NaN                        NA    \n",
              "5                         NaN                        NA    \n",
              "6                         NaN                        NA    \n",
              "7                         NaN                        NA    \n",
              "\n",
              "   Agriculture (before process) Agriculture (after process)   \\\n",
              "0                           NaN                          NA    \n",
              "1                           1.0                            1   \n",
              "2                           NaN                          NA    \n",
              "3                           NaN                          NA    \n",
              "4                           NaN                          NA    \n",
              "5                           NaN                          NA    \n",
              "6                           NaN                          NA    \n",
              "7                           NaN                          NA    \n",
              "\n",
              "   Hydropower (before process) Hydropower (after process)   \\\n",
              "0                          NaN                         NA    \n",
              "1                          NaN                         NA    \n",
              "2                          NaN                         NA    \n",
              "3                          NaN                         NA    \n",
              "4                          NaN                         NA    \n",
              "5                          NaN                         NA    \n",
              "6                          NaN                         NA    \n",
              "7                          NaN                         NA    \n",
              "\n",
              "   Energy security (before process) Energy security (after process)   \\\n",
              "0                               NaN                              NA    \n",
              "1                               5.0                                5   \n",
              "2                               NaN                              NA    \n",
              "3                               NaN                              NA    \n",
              "4                               NaN                              NA    \n",
              "5                               NaN                              NA    \n",
              "6                               NaN                              NA    \n",
              "7                               NaN                              NA    \n",
              "\n",
              "   Finances (before process) Finances (after process)   \\\n",
              "0                        NaN                       NA    \n",
              "1                        0.0                         0   \n",
              "2                        NaN                       NA    \n",
              "3                        NaN                       NA    \n",
              "4                        NaN                       NA    \n",
              "5                        NaN                       NA    \n",
              "6                        NaN                       NA    \n",
              "7                        NaN                       NA    \n",
              "\n",
              "   Employment (before process) Employment (after process)   \\\n",
              "0                          NaN                         NA    \n",
              "1                          5.0                           5   \n",
              "2                          NaN                         NA    \n",
              "3                          NaN                         NA    \n",
              "4                          NaN                         NA    \n",
              "5                          NaN                         NA    \n",
              "6                          NaN                         NA    \n",
              "7                          NaN                         NA    \n",
              "\n",
              "   Status Quo of power relations (before process)  \\\n",
              "0                                             NaN   \n",
              "1                                             NaN   \n",
              "2                                             NaN   \n",
              "3                                             NaN   \n",
              "4                                             NaN   \n",
              "5                                             NaN   \n",
              "6                                             NaN   \n",
              "7                                             NaN   \n",
              "\n",
              "  Status Quo of power relations (after process)   \n",
              "0                                            NA   \n",
              "1                                            NA   \n",
              "2                                            NA   \n",
              "3                                            NA   \n",
              "4                                            NA   \n",
              "5                                            NA   \n",
              "6                                            NA   \n",
              "7                                            NA   "
            ],
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              "      <th>Actor 2</th>\n",
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              "    </tr>\n",
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              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NA</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------------------------------------------\n",
            "\n",
            "📄 Full DataFrame for Savognin Actor: Actor 3A\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  Actor 3_a  Biodiversity (before process) Biodiversity (after process)   \\\n",
              "0   Actor 1                            NaN                           NaN   \n",
              "1   Actor 2                            NaN                           NaN   \n",
              "2   Actor 3                            1.0                             1   \n",
              "3   Actor 4                            NaN                           NaN   \n",
              "4   Actor 5                            NaN                           NaN   \n",
              "5   Actor 6                            NaN                           NaN   \n",
              "6   Actor 7                            NaN                           NA    \n",
              "7   Actor 8                            NaN                           NA    \n",
              "\n",
              "   Landscape (before process) Landscape (after process)   \\\n",
              "0                         NaN                        NaN   \n",
              "1                         NaN                        NaN   \n",
              "2                         1.0                          1   \n",
              "3                         NaN                        NaN   \n",
              "4                         NaN                        NaN   \n",
              "5                         NaN                        NaN   \n",
              "6                         NaN                        NA    \n",
              "7                         NaN                        NA    \n",
              "\n",
              "   Agriculture (before process) Agriculture (after process)   \\\n",
              "0                           NaN                          NaN   \n",
              "1                           NaN                          NaN   \n",
              "2                           1.0                            1   \n",
              "3                           NaN                          NaN   \n",
              "4                           NaN                          NaN   \n",
              "5                           NaN                          NaN   \n",
              "6                           NaN                          NA    \n",
              "7                           NaN                          NA    \n",
              "\n",
              "   Hydropower (before process) Hydropower (after process)   \\\n",
              "0                          NaN                         NaN   \n",
              "1                          NaN                         NaN   \n",
              "2                          5.0                           5   \n",
              "3                          NaN                         NaN   \n",
              "4                          NaN                         NaN   \n",
              "5                          NaN                         NaN   \n",
              "6                          NaN                         NA    \n",
              "7                          NaN                         NA    \n",
              "\n",
              "   Energy security (before process) Energy security (after process)   \\\n",
              "0                               NaN                              NaN   \n",
              "1                               NaN                              NaN   \n",
              "2                               5.0                                5   \n",
              "3                               NaN                              NaN   \n",
              "4                               NaN                              NaN   \n",
              "5                               NaN                              NaN   \n",
              "6                               NaN                              NA    \n",
              "7                               NaN                              NA    \n",
              "\n",
              "   Finances (before process) Finances (after process)   \\\n",
              "0                        NaN                       NaN   \n",
              "1                        NaN                       NaN   \n",
              "2                        1.0                         1   \n",
              "3                        NaN                       NaN   \n",
              "4                        NaN                       NaN   \n",
              "5                        NaN                       NaN   \n",
              "6                        NaN                       NA    \n",
              "7                        NaN                       NA    \n",
              "\n",
              "   Employment (before process) Employment (after process)   \\\n",
              "0                          NaN                         NaN   \n",
              "1                          NaN                         NaN   \n",
              "2                          5.0                           5   \n",
              "3                          NaN                         NaN   \n",
              "4                          NaN                         NaN   \n",
              "5                          NaN                         NaN   \n",
              "6                          NaN                         NA    \n",
              "7                          NaN                         NA    \n",
              "\n",
              "   Status Quo of power relations (before process)  \\\n",
              "0                                             NaN   \n",
              "1                                             NaN   \n",
              "2                                             5.0   \n",
              "3                                             NaN   \n",
              "4                                             NaN   \n",
              "5                                             NaN   \n",
              "6                                             NaN   \n",
              "7                                             NaN   \n",
              "\n",
              "  Status Quo of power relations (after process)   \n",
              "0                                            NaN  \n",
              "1                                            NaN  \n",
              "2                                              5  \n",
              "3                                            NaN  \n",
              "4                                            NaN  \n",
              "5                                            NaN  \n",
              "6                                            NA   \n",
              "7                                            NA   "
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Actor 3</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>Actor 4</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Actor 7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NA</td>\n",
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              "      <td>NA</td>\n",
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          "text": [
            "------------------------------------------------------------\n",
            "\n",
            "📄 Full DataFrame for Savognin Actor: Actor 3B\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  Actor 3_b Biodiversity (before process) Biodiversity (after process)   \\\n",
              "0   Actor 1                           NA                            NA    \n",
              "1   Actor 2                           NA                            NA    \n",
              "2   Actor 3                             5                             1   \n",
              "3   Actor 4                           NA                            NaN   \n",
              "4   Actor 5                           NA                            NaN   \n",
              "5   Actor 6                           NA                            NaN   \n",
              "6   Actor 7                           NA                            NaN   \n",
              "7   Actor 8                           NA                            NaN   \n",
              "\n",
              "  Landscape (before process) Landscape (after process)   \\\n",
              "0                          1                          1   \n",
              "1                        NA                         NA    \n",
              "2                          5                          1   \n",
              "3                        NA                         NaN   \n",
              "4                        NA                         NaN   \n",
              "5                        NA                         NaN   \n",
              "6                        NA                         NaN   \n",
              "7                        NA                         NaN   \n",
              "\n",
              "  Agriculture (before process) Agriculture (after process)   \\\n",
              "0                          NaN                          NA    \n",
              "1                            1                            1   \n",
              "2                            1                            1   \n",
              "3                          NA                           NaN   \n",
              "4                          NA                           NaN   \n",
              "5                          NA                           NaN   \n",
              "6                          NA                           NaN   \n",
              "7                          NA                           NaN   \n",
              "\n",
              "  Hydropower (before process) Hydropower (after process)   \\\n",
              "0                         NaN                         NA    \n",
              "1                         NA                          NA    \n",
              "2                         NaN                         NA    \n",
              "3                         NA                          NaN   \n",
              "4                         NA                          NaN   \n",
              "5                         NA                          NaN   \n",
              "6                         NA                          NaN   \n",
              "7                         NA                          NaN   \n",
              "\n",
              "  Energy security (before process) Energy security (after process)   \\\n",
              "0                              NaN                              NA    \n",
              "1                              NA                               NA    \n",
              "2                                0                                0   \n",
              "3                              NA                               NaN   \n",
              "4                              NA                               NaN   \n",
              "5                              NA                               NaN   \n",
              "6                              NA                               NaN   \n",
              "7                              NA                               NaN   \n",
              "\n",
              "  Finances (before process) Finances (after process)   \\\n",
              "0                       NaN                       NA    \n",
              "1                         0                         0   \n",
              "2                         1                         1   \n",
              "3                       NA                        NaN   \n",
              "4                       NA                        NaN   \n",
              "5                       NA                        NaN   \n",
              "6                       NA                        NaN   \n",
              "7                       NA                        NaN   \n",
              "\n",
              "  Employment (before process) Employment (after process)   \\\n",
              "0                         NaN                         NA    \n",
              "1                         NA                          NA    \n",
              "2                           5                           5   \n",
              "3                         NA                          NaN   \n",
              "4                         NA                          NaN   \n",
              "5                         NA                          NaN   \n",
              "6                         NA                          NaN   \n",
              "7                         NA                          NaN   \n",
              "\n",
              "  Status Quo of power relations (before process)  \\\n",
              "0                                            NaN   \n",
              "1                                            NA    \n",
              "2                                              1   \n",
              "3                                            NA    \n",
              "4                                            NA    \n",
              "5                                            NA    \n",
              "6                                            NA    \n",
              "7                                            NA    \n",
              "\n",
              "  Status Quo of power relations (after process)   \n",
              "0                                            NA   \n",
              "1                                            NA   \n",
              "2                                              1  \n",
              "3                                            NaN  \n",
              "4                                            NaN  \n",
              "5                                            NaN  \n",
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              "      <th>Energy security (before process)</th>\n",
              "      <th>Energy security (after process)</th>\n",
              "      <th>Finances (before process)</th>\n",
              "      <th>Finances (after process)</th>\n",
              "      <th>Employment (before process)</th>\n",
              "      <th>Employment (after process)</th>\n",
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              "      <td>NA</td>\n",
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              "      <td>NA</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Actor 2</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Actor 3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
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              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
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              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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              "      <th>5</th>\n",
              "      <td>Actor 6</td>\n",
              "      <td>NA</td>\n",
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              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
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              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
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              "      <td>NA</td>\n",
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              "    <tr>\n",
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              "      <td>NA</td>\n",
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              "      <td>NA</td>\n",
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------------------------------------------\n",
            "\n",
            "📄 Full DataFrame for Savognin Actor: Actor 3C\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  Actor 3_c Biodiversity (before process) Biodiversity (after process)   \\\n",
              "0   Actor 1                             1                             1   \n",
              "1   Actor 2                           NA                            NA    \n",
              "2   Actor 3                             1                             1   \n",
              "3   Actor 4                           NA                            NA    \n",
              "4   Actor 5                           NA                            NA    \n",
              "5   Actor 6                           NA                            NA    \n",
              "6   Actor 7                           NaN                           NaN   \n",
              "7   Actor 8                           NaN                           NaN   \n",
              "\n",
              "  Landscape (before process) Landscape (after process)   \\\n",
              "0                          1                          1   \n",
              "1                        NA                         NA    \n",
              "2                          1                          1   \n",
              "3                        NA                         NA    \n",
              "4                        NA                         NA    \n",
              "5                        NA                         NA    \n",
              "6                        NaN                        NaN   \n",
              "7                        NaN                        NaN   \n",
              "\n",
              "  Agriculture (before process) Agriculture (after process)   \\\n",
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\"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Energy security (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Finances (before process)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Finances (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Employment (before process)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Employment (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Status Quo of power relations (before process)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Status Quo of power relations (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {}
        },
        {
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          "name": "stdout",
          "text": [
            "------------------------------------------------------------\n",
            "\n",
            "📄 Full DataFrame for Savognin Actor: Actor 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Actor 4  Biodiversity (before process) Biodiversity (after process)   \\\n",
              "0  Actor 1                            1.0                             1   \n",
              "1  Actor 2                            5.0                             5   \n",
              "2  Actor 3                            1.0                             1   \n",
              "3  Actor 4                            5.0                             5   \n",
              "4  Actor 5                            NaN                           NA    \n",
              "5  Actor 6                            5.0                             5   \n",
              "6  Actor 7                            NaN                           NA    \n",
              "7  Actor 8                            NaN                           NA    \n",
              "\n",
              "   Landscape (before process) Landscape (after process)   \\\n",
              "0                         1.0                          1   \n",
              "1                         5.0                          5   \n",
              "2                         5.0                          1   \n",
              "3                         1.0                          1   \n",
              "4                         NaN                        NA    \n",
              "5                         5.0                          5   \n",
              "6                         NaN                        NA    \n",
              "7                         NaN                        NA    \n",
              "\n",
              "   Agriculture (before process) Agriculture (after process)   \\\n",
              "0                           5.0                            5   \n",
              "1                           1.0                            1   \n",
              "2                           5.0                            1   \n",
              "3                           5.0                            5   \n",
              "4                           NaN                          NA    \n",
              "5                           5.0                            5   \n",
              "6                           NaN                          NA    \n",
              "7                           NaN                          NA    \n",
              "\n",
              "   Hydropower (before process) Hydropower (after process)   \\\n",
              "0                          5.0                           5   \n",
              "1                          5.0                           5   \n",
              "2                          5.0                           5   \n",
              "3                          5.0                           5   \n",
              "4                          NaN                         NA    \n",
              "5                          5.0                           5   \n",
              "6                          NaN                         NA    \n",
              "7                          NaN                         NA    \n",
              "\n",
              "   Energy security (before process) Energy security (after process)   \\\n",
              "0                               0.0                                5   \n",
              "1                               0.0                                5   \n",
              "2                               0.0                                5   \n",
              "3                               0.0                                0   \n",
              "4                               NaN                              NA    \n",
              "5                               0.0                                5   \n",
              "6                               NaN                              NA    \n",
              "7                               NaN                              NA    \n",
              "\n",
              "   Finances (before process) Finances (after process)   \\\n",
              "0                        0.0                         0   \n",
              "1                        0.0                         0   \n",
              "2                        0.0                         0   \n",
              "3                        0.0                         0   \n",
              "4                        NaN                       NA    \n",
              "5                        0.0                         0   \n",
              "6                        NaN                       NA    \n",
              "7                        NaN                       NA    \n",
              "\n",
              "   Employment (before process) Employment (after process)   \\\n",
              "0                          5.0                           5   \n",
              "1                          5.0                           5   \n",
              "2                          5.0                           5   \n",
              "3                          0.0                           0   \n",
              "4                          NaN                         NA    \n",
              "5                          5.0                           5   \n",
              "6                          NaN                         NA    \n",
              "7                          NaN                         NA    \n",
              "\n",
              "   Status Quo of power relations (before process)  \\\n",
              "0                                             NaN   \n",
              "1                                             NaN   \n",
              "2                                             NaN   \n",
              "3                                             5.0   \n",
              "4                                             NaN   \n",
              "5                                             NaN   \n",
              "6                                             NaN   \n",
              "7                                             NaN   \n",
              "\n",
              "  Status Quo of power relations (after process)   \n",
              "0                                            NA   \n",
              "1                                            NA   \n",
              "2                                            NaN  \n",
              "3                                              5  \n",
              "4                                            NA   \n",
              "5                                            NaN  \n",
              "6                                            NA   \n",
              "7                                            NA   "
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          "metadata": {}
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        {
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          "name": "stdout",
          "text": [
            "------------------------------------------------------------\n",
            "\n",
            "📄 Full DataFrame for Savognin Actor: Actor 6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Actor 6  Biodiversity (before process) Biodiversity (after process)   \\\n",
              "0  Actor 1                            5.0                             5   \n",
              "1  Actor 2                            1.0                             1   \n",
              "2  Actor 3                            NaN                           NaN   \n",
              "3  Actor 4                            NaN                           NaN   \n",
              "4  Actor 5                            NaN                           NaN   \n",
              "5  Actor 6                            5.0                             5   \n",
              "6  Actor 7                            NaN                           NA    \n",
              "7  Actor 8                            NaN                           NA    \n",
              "\n",
              "   Landscape (before process) Landscape (after process)   \\\n",
              "0                         1.0                          1   \n",
              "1                         1.0                          1   \n",
              "2                         NaN                        NaN   \n",
              "3                         NaN                        NaN   \n",
              "4                         NaN                        NaN   \n",
              "5                         5.0                          5   \n",
              "6                         NaN                        NA    \n",
              "7                         NaN                        NA    \n",
              "\n",
              "   Agriculture (before process) Agriculture (after process)   \\\n",
              "0                           5.0                            5   \n",
              "1                           1.0                            1   \n",
              "2                           NaN                          NaN   \n",
              "3                           NaN                          NaN   \n",
              "4                           NaN                          NaN   \n",
              "5                           5.0                            5   \n",
              "6                           NaN                          NA    \n",
              "7                           NaN                          NA    \n",
              "\n",
              "   Hydropower (before process) Hydropower (after process)   \\\n",
              "0                          NaN                         NA    \n",
              "1                          NaN                         NA    \n",
              "2                          NaN                         NaN   \n",
              "3                          NaN                         NaN   \n",
              "4                          NaN                         NaN   \n",
              "5                          NaN                         NaN   \n",
              "6                          NaN                         NA    \n",
              "7                          NaN                         NA    \n",
              "\n",
              "   Energy security (before process) Energy security (after process)   \\\n",
              "0                               5.0                                5   \n",
              "1                               NaN                              NA    \n",
              "2                               NaN                              NaN   \n",
              "3                               NaN                              NaN   \n",
              "4                               NaN                              NaN   \n",
              "5                               0.0                                0   \n",
              "6                               NaN                              NA    \n",
              "7                               NaN                              NA    \n",
              "\n",
              "   Finances (before process) Finances (after process)   \\\n",
              "0                        5.0                         5   \n",
              "1                        NaN                       NA    \n",
              "2                        NaN                       NaN   \n",
              "3                        NaN                       NaN   \n",
              "4                        NaN                       NaN   \n",
              "5                        0.0                         0   \n",
              "6                        NaN                       NA    \n",
              "7                        NaN                       NA    \n",
              "\n",
              "   Employment (before process) Employment (after process)   \\\n",
              "0                          NaN                         NA    \n",
              "1                          NaN                         NA    \n",
              "2                          NaN                         NaN   \n",
              "3                          NaN                         NaN   \n",
              "4                          NaN                         NaN   \n",
              "5                          0.0                           0   \n",
              "6                          NaN                         NA    \n",
              "7                          NaN                         NA    \n",
              "\n",
              "   Status Quo of power relations (before process)  \\\n",
              "0                                             NaN   \n",
              "1                                             NaN   \n",
              "2                                             NaN   \n",
              "3                                             NaN   \n",
              "4                                             NaN   \n",
              "5                                             NaN   \n",
              "6                                             NaN   \n",
              "7                                             NaN   \n",
              "\n",
              "  Status Quo of power relations (after process)   \n",
              "0                                            NA   \n",
              "1                                            NA   \n",
              "2                                            NaN  \n",
              "3                                            NaN  \n",
              "4                                            NaN  \n",
              "5                                            NaN  \n",
              "6                                            NA   \n",
              "7                                            NA   "
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "summary": "{\n  \"name\": \"preview_dataframes(list_cleaned_sedrun, '/content/Sedrun', 'Sedrun')\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Actor 6\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Actor 2\",\n          \"Actor 6\",\n          \"Actor 1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Biodiversity (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3094010767585034,\n        \"min\": 1.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Biodiversity (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        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            }
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------------------------------------------\n",
            "\n",
            "📄 Full DataFrame for Savognin Actor: Actor 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Actor 8  Biodiversity (before process) Biodiversity (after process)   \\\n",
              "0  Actor 1                            5.0                             1   \n",
              "1  Actor 2                            NaN                           NA    \n",
              "2  Actor 3                            1.0                             1   \n",
              "3  Actor 4                            NaN                           NaN   \n",
              "4  Actor 5                            NaN                           NaN   \n",
              "5  Actor 6                            NaN                           NaN   \n",
              "6  Actor 7                            NaN                           NaN   \n",
              "7  Actor 8                            0.0                             0   \n",
              "\n",
              "   Landscape (before process) Landscape (after process)   \\\n",
              "0                         1.0                          1   \n",
              "1                         NaN                        NA    \n",
              "2                         5.0                          1   \n",
              "3                         NaN                        NaN   \n",
              "4                         NaN                        NaN   \n",
              "5                         NaN                        NaN   \n",
              "6                         NaN                        NaN   \n",
              "7                         1.0                          1   \n",
              "\n",
              "  Agriculture (before process) Agriculture (after process)   \\\n",
              "0                          NA                           NA    \n",
              "1                          NaN                          NaN   \n",
              "2                          NaN                          NA    \n",
              "3                          NaN                          NaN   \n",
              "4                          NaN                          NaN   \n",
              "5                          NaN                          NaN   \n",
              "6                          NaN                          NaN   \n",
              "7                            0                            0   \n",
              "\n",
              "   Hydropower (before process) Hydropower (after process)   \\\n",
              "0                          NaN                         NA    \n",
              "1                          NaN                         NA    \n",
              "2                          NaN                         NA    \n",
              "3                          NaN                         NaN   \n",
              "4                          NaN                         NaN   \n",
              "5                          NaN                         NaN   \n",
              "6                          NaN                         NaN   \n",
              "7                          5.0                           5   \n",
              "\n",
              "   Energy security (before process) Energy security (after process)   \\\n",
              "0                               NaN                              NA    \n",
              "1                               NaN                              NA    \n",
              "2                               NaN                              NA    \n",
              "3                               NaN                              NaN   \n",
              "4                               NaN                              NaN   \n",
              "5                               NaN                              NaN   \n",
              "6                               NaN                              NaN   \n",
              "7                               0.0                                0   \n",
              "\n",
              "   Finances (before process) Finances (after process)   \\\n",
              "0                        NaN                       NA    \n",
              "1                        NaN                       NA    \n",
              "2                        NaN                       NA    \n",
              "3                        0.0                         0   \n",
              "4                        NaN                       NaN   \n",
              "5                        NaN                       NaN   \n",
              "6                        NaN                       NaN   \n",
              "7                        0.0                         0   \n",
              "\n",
              "   Employment (before process) Employment (after process)   \\\n",
              "0                          NaN                         NA    \n",
              "1                          NaN                         NA    \n",
              "2                          NaN                         NA    \n",
              "3                          NaN                         NA    \n",
              "4                          NaN                         NaN   \n",
              "5                          NaN                         NaN   \n",
              "6                          NaN                         NaN   \n",
              "7                          0.0                           0   \n",
              "\n",
              "   Status Quo of power relations (before process)  \\\n",
              "0                                             NaN   \n",
              "1                                             NaN   \n",
              "2                                             NaN   \n",
              "3                                             NaN   \n",
              "4                                             NaN   \n",
              "5                                             NaN   \n",
              "6                                             NaN   \n",
              "7                                             0.0   \n",
              "\n",
              "  Status Quo of power relations (after process)   \n",
              "0                                            NA   \n",
              "1                                            NA   \n",
              "2                                            NA   \n",
              "3                                            NA   \n",
              "4                                            NaN  \n",
              "5                                            NaN  \n",
              "6                                            NaN  \n",
              "7                                              0  "
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              "\n",
              "  <div id=\"df-001a0183-c812-4945-9e6d-ec3e8140e009\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>Actor 8</th>\n",
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              "      <th>Status Quo of power relations (after process)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Actor 1</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <th>3</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>NaN</td>\n",
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          "text": [
            "------------------------------------------------------------\n",
            "\n",
            "📄 Full DataFrame for Sedrun Actor: Actor 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Actor 1  Biodiversity (before process) Biodiversity (after process)   \\\n",
              "0  Actor 1                            5.0                             5   \n",
              "1  Actor 2                            NaN                           NaN   \n",
              "2  Actor 3                            NaN                           NA    \n",
              "3  Actor 4                            NaN                           NA    \n",
              "4  Actor 5                            NaN                           NA    \n",
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              "\n",
              "   Landscape (before process) Landscape (after process)   \\\n",
              "0                         5.0                          5   \n",
              "1                         NaN                        NaN   \n",
              "2                         NaN                        NA    \n",
              "3                         NaN                        NA    \n",
              "4                         NaN                        NA    \n",
              "5                         NaN                        NaN   \n",
              "6                         NaN                        NA    \n",
              "7                         NaN                        NA    \n",
              "\n",
              "   Agriculture (before process) Agriculture (after process)   \\\n",
              "0                           5.0                            0   \n",
              "1                           1.0                            5   \n",
              "2                           NaN                          NA    \n",
              "3                           NaN                          NA    \n",
              "4                           NaN                          NA    \n",
              "5                           NaN                          NaN   \n",
              "6                           NaN                          NA    \n",
              "7                           NaN                          NA    \n",
              "\n",
              "   Hydropower (before process) Hydropower (after process)   \\\n",
              "0                          5.0                           5   \n",
              "1                          NaN                         NaN   \n",
              "2                          NaN                         NA    \n",
              "3                          NaN                         NA    \n",
              "4                          NaN                         NA    \n",
              "5                          NaN                         NaN   \n",
              "6                          NaN                         NA    \n",
              "7                          NaN                         NA    \n",
              "\n",
              "   Energy security (before process) Energy security (after process)   \\\n",
              "0                               0.0                                0   \n",
              "1                               NaN                              NaN   \n",
              "2                               NaN                              NA    \n",
              "3                               NaN                              NA    \n",
              "4                               NaN                              NA    \n",
              "5                               NaN                              NaN   \n",
              "6                               NaN                              NA    \n",
              "7                               NaN                              NA    \n",
              "\n",
              "   Finances (before process) Finances (after process)   \\\n",
              "0                        5.0                         5   \n",
              "1                        NaN                       NaN   \n",
              "2                        NaN                       NA    \n",
              "3                        NaN                       NA    \n",
              "4                        NaN                       NA    \n",
              "5                        NaN                       NaN   \n",
              "6                        NaN                       NA    \n",
              "7                        NaN                       NA    \n",
              "\n",
              "   Employment (before process) Employment (after process)   \\\n",
              "0                          0.0                           0   \n",
              "1                          NaN                         NaN   \n",
              "2                          NaN                         NA    \n",
              "3                          NaN                         NA    \n",
              "4                          NaN                         NA    \n",
              "5                          0.0                           0   \n",
              "6                          NaN                         NA    \n",
              "7                          NaN                         NA    \n",
              "\n",
              "   Status Quo of power relations (before process)  \\\n",
              "0                                             NaN   \n",
              "1                                             NaN   \n",
              "2                                             NaN   \n",
              "3                                             NaN   \n",
              "4                                             NaN   \n",
              "5                                             NaN   \n",
              "6                                             NaN   \n",
              "7                                             NaN   \n",
              "\n",
              "  Status Quo of power relations (after process)   \n",
              "0                                            NA   \n",
              "1                                            NaN  \n",
              "2                                            NA   \n",
              "3                                            NA   \n",
              "4                                            NA   \n",
              "5                                            NA   \n",
              "6                                            NA   \n",
              "7                                            NA   "
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              "    <tr>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
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              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
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              "summary": "{\n  \"name\": \"preview_dataframes(list_cleaned_sedrun, '/content/Sedrun', 'Sedrun')\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Actor 1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Actor 2\",\n          \"Actor 6\",\n          \"Actor 1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Biodiversity (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 5.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Biodiversity (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": 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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------------------------------------------\n",
            "\n",
            "📄 Full DataFrame for Sedrun Actor: Actor 3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Actor 3  Biodiversity (before process)  Biodiversity (after process)   \\\n",
              "0  Actor 1                            NaN                            NaN   \n",
              "1  Actor 2                            5.0                            5.0   \n",
              "2  Actor 3                            5.0                            0.0   \n",
              "3  Actor 4                            NaN                            NaN   \n",
              "4  Actor 5                            NaN                            NaN   \n",
              "5  Actor 6                            NaN                            NaN   \n",
              "6  Actor 7                            NaN                            NaN   \n",
              "7  Actor 8                            NaN                            NaN   \n",
              "\n",
              "   Landscape (before process)  Landscape (after process)   \\\n",
              "0                         NaN                         NaN   \n",
              "1                         5.0                         5.0   \n",
              "2                         5.0                         5.0   \n",
              "3                         NaN                         NaN   \n",
              "4                         NaN                         NaN   \n",
              "5                         NaN                         NaN   \n",
              "6                         NaN                         NaN   \n",
              "7                         NaN                         NaN   \n",
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              "   Agriculture (before process)  Agriculture (after process)   \\\n",
              "0                           NaN                           NaN   \n",
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              "2                           5.0                           5.0   \n",
              "3                           NaN                           NaN   \n",
              "4                           NaN                           NaN   \n",
              "5                           NaN                           NaN   \n",
              "6                           NaN                           NaN   \n",
              "7                           5.0                           5.0   \n",
              "\n",
              "   Hydropower (before process) Hydropower (after process)   \\\n",
              "0                          NaN                         NaN   \n",
              "1                          NaN                         NaN   \n",
              "2                          NaN                         NA    \n",
              "3                          NaN                         NaN   \n",
              "4                          NaN                         NaN   \n",
              "5                          NaN                         NaN   \n",
              "6                          NaN                         NaN   \n",
              "7                          NaN                         NaN   \n",
              "\n",
              "   Energy security (before process)  Energy security (after process)   \\\n",
              "0                               NaN                               NaN   \n",
              "1                               NaN                               NaN   \n",
              "2                               0.0                               0.0   \n",
              "3                               NaN                               NaN   \n",
              "4                               NaN                               NaN   \n",
              "5                               NaN                               NaN   \n",
              "6                               NaN                               NaN   \n",
              "7                               NaN                               NaN   \n",
              "\n",
              "   Finances (before process)  Finances (after process)   \\\n",
              "0                        NaN                        NaN   \n",
              "1                        NaN                        NaN   \n",
              "2                        5.0                        5.0   \n",
              "3                        NaN                        NaN   \n",
              "4                        NaN                        NaN   \n",
              "5                        NaN                        NaN   \n",
              "6                        NaN                        NaN   \n",
              "7                        NaN                        NaN   \n",
              "\n",
              "   Employment (before process)  Employment (after process)   \\\n",
              "0                          NaN                          NaN   \n",
              "1                          NaN                          NaN   \n",
              "2                          5.0                          5.0   \n",
              "3                          NaN                          NaN   \n",
              "4                          NaN                          NaN   \n",
              "5                          NaN                          NaN   \n",
              "6                          NaN                          NaN   \n",
              "7                          NaN                          NaN   \n",
              "\n",
              "   Status Quo of power relations (before process)  \\\n",
              "0                                             NaN   \n",
              "1                                             NaN   \n",
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              "3                                             NaN   \n",
              "4                                             NaN   \n",
              "5                                             NaN   \n",
              "6                                             NaN   \n",
              "7                                             NaN   \n",
              "\n",
              "   Status Quo of power relations (after process)   \n",
              "0                                             NaN  \n",
              "1                                             NaN  \n",
              "2                                             5.0  \n",
              "3                                             NaN  \n",
              "4                                             NaN  \n",
              "5                                             NaN  \n",
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              "7                                             NaN  "
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              "      <th>Employment (before process)</th>\n",
              "      <th>Employment (after process)</th>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>5.0</td>\n",
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              "      <td>5.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------------------------------------------\n",
            "\n",
            "📄 Full DataFrame for Sedrun Actor: Actor 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Actor 4 Biodiversity (before process) Biodiversity (after process)   \\\n",
              "0  Actor 1                             5                             5   \n",
              "1  Actor 2                             5                             5   \n",
              "2  Actor 3                             1                             5   \n",
              "3  Actor 4                             5                             0   \n",
              "4  Actor 5                           NA                            NA    \n",
              "5  Actor 6                           NA                            NA    \n",
              "6  Actor 7                           NA                            NA    \n",
              "7  Actor 8                           NA                            NA    \n",
              "\n",
              "  Landscape (before process) Landscape (after process)   \\\n",
              "0                          1                          1   \n",
              "1                          5                          5   \n",
              "2                          1                          1   \n",
              "3                          1                          1   \n",
              "4                        NA                         NA    \n",
              "5                        NA                         NA    \n",
              "6                        NA                         NA    \n",
              "7                        NA                         NA    \n",
              "\n",
              "  Agriculture (before process) Agriculture (after process)   \\\n",
              "0                            5                            5   \n",
              "1                            5                            0   \n",
              "2                          NA                           NA    \n",
              "3                            5                            0   \n",
              "4                          NA                           NA    \n",
              "5                          NA                           NA    \n",
              "6                          NA                           NA    \n",
              "7                          NA                           NA    \n",
              "\n",
              "  Hydropower (before process) Hydropower (after process)   \\\n",
              "0                           5                           5   \n",
              "1                           5                           5   \n",
              "2                         NA                          NA    \n",
              "3                           5                           5   \n",
              "4                         NA                          NA    \n",
              "5                         NA                          NA    \n",
              "6                         NA                          NA    \n",
              "7                         NA                          NA    \n",
              "\n",
              "  Energy security (before process) Energy security (after process)   \\\n",
              "0                                5                                5   \n",
              "1                                5                                5   \n",
              "2                              NA                               NA    \n",
              "3                                0                                0   \n",
              "4                              NA                               NA    \n",
              "5                              NA                               NA    \n",
              "6                              NA                               NA    \n",
              "7                              NA                               NA    \n",
              "\n",
              "  Finances (before process) Finances (after process)   \\\n",
              "0                         5                         5   \n",
              "1                         5                         0   \n",
              "2                       NA                        NA    \n",
              "3                         0                         0   \n",
              "4                         0                         0   \n",
              "5                         0                         0   \n",
              "6                       NA                        NA    \n",
              "7                       NA                        NA    \n",
              "\n",
              "  Employment (before process) Employment (after process)   \\\n",
              "0                           0                           0   \n",
              "1                           5                           5   \n",
              "2                         NA                          NA    \n",
              "3                           0                           0   \n",
              "4                           0                           0   \n",
              "5                           0                           0   \n",
              "6                         NA                          NA    \n",
              "7                         NA                          NA    \n",
              "\n",
              "  Status Quo of power relations (before process)  \\\n",
              "0                                            NA    \n",
              "1                                            NaN   \n",
              "2                                            NA    \n",
              "3                                              0   \n",
              "4                                            NaN   \n",
              "5                                            NaN   \n",
              "6                                            NA    \n",
              "7                                            NA    \n",
              "\n",
              "  Status Quo of power relations (after process)   \n",
              "0                                            NaN  \n",
              "1                                            NaN  \n",
              "2                                            NA   \n",
              "3                                              0  \n",
              "4                                            NaN  \n",
              "5                                            NaN  \n",
              "6                                            NA   \n",
              "7                                            NA   "
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(before process)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Energy security (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Finances (before process)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Finances (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        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          "text": [
            "------------------------------------------------------------\n",
            "\n",
            "📄 Full DataFrame for Sedrun Actor: Actor 5\n"
          ]
        },
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              "   Actor 5 Biodiversity (before process) Biodiversity (after process)   \\\n",
              "0  Actor 1                           NA                            NA    \n",
              "1  Actor 2                           NA                            NaN   \n",
              "2  Actor 3                             5                             5   \n",
              "3  Actor 4                           NA                            NaN   \n",
              "4  Actor 5                             5                             0   \n",
              "5  Actor 6                           NA                            NaN   \n",
              "6  Actor 7                           NaN                           NA    \n",
              "7  Actor 8                           NaN                           NA    \n",
              "\n",
              "   Landscape (before process) Landscape (after process)   \\\n",
              "0                         1.0                          1   \n",
              "1                         1.0                          1   \n",
              "2                         1.0                          1   \n",
              "3                         1.0                          1   \n",
              "4                         1.0                          1   \n",
              "5                         1.0                          1   \n",
              "6                         NaN                        NA    \n",
              "7                         NaN                        NA    \n",
              "\n",
              "  Agriculture (before process) Agriculture (after process)   \\\n",
              "0                          NaN                          NA    \n",
              "1                            5                            0   \n",
              "2                            5                            5   \n",
              "3                          NA                           NaN   \n",
              "4                            5                            5   \n",
              "5                          NA                           NaN   \n",
              "6                          NaN                          NA    \n",
              "7                          NaN                          NA    \n",
              "\n",
              "  Hydropower (before process) Hydropower (after process)   \\\n",
              "0                         NaN                         NA    \n",
              "1                         NA                          NaN   \n",
              "2                         NA                          NaN   \n",
              "3                         NA                          NaN   \n",
              "4                           5                           5   \n",
              "5                         NA                          NaN   \n",
              "6                         NaN                         NA    \n",
              "7                         NaN                         NA    \n",
              "\n",
              "  Energy security (before process) Energy security (after process)   \\\n",
              "0                              NaN                              NA    \n",
              "1                              NA                               NaN   \n",
              "2                                0                                0   \n",
              "3                              NA                               NaN   \n",
              "4                                0                                0   \n",
              "5                              NA                               NaN   \n",
              "6                              NaN                              NA    \n",
              "7                              NaN                              NA    \n",
              "\n",
              "  Finances (before process) Finances (after process)   \\\n",
              "0                       NaN                       NA    \n",
              "1                         5                         0   \n",
              "2                       NA                        NaN   \n",
              "3                       NA                        NaN   \n",
              "4                         0                         0   \n",
              "5                       NA                        NaN   \n",
              "6                       NaN                       NA    \n",
              "7                       NaN                       NA    \n",
              "\n",
              "  Employment (before process) Employment (after process)   \\\n",
              "0                         NaN                         NA    \n",
              "1                         NaN                         NaN   \n",
              "2                         NA                          NaN   \n",
              "3                           5                           5   \n",
              "4                           0                           0   \n",
              "5                           5                           5   \n",
              "6                         NaN                         NA    \n",
              "7                         NaN                         NA    \n",
              "\n",
              "  Status Quo of power relations (before process)  \\\n",
              "0                                            NaN   \n",
              "1                                            NaN   \n",
              "2                                            NA    \n",
              "3                                              0   \n",
              "4                                              0   \n",
              "5                                            NaN   \n",
              "6                                            NaN   \n",
              "7                                            NaN   \n",
              "\n",
              "  Status Quo of power relations (after process)   \n",
              "0                                            NA   \n",
              "1                                            NaN  \n",
              "2                                            NaN  \n",
              "3                                              0  \n",
              "4                                              0  \n",
              "5                                            NaN  \n",
              "6                                            NA   \n",
              "7                                            NA   "
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              "  }\n",
              "\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        })();\n",
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              "\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"preview_dataframes(list_cleaned_sedrun, '/content/Sedrun', 'Sedrun')\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Actor 5\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Actor 2\",\n          \"Actor 6\",\n          \"Actor 1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Biodiversity (before process)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5,\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Biodiversity (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"NA \",\n          5\n        ],\n  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}\n    },\n    {\n      \"column\": \"Agriculture (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hydropower (before process)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hydropower (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Energy security (before process)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " # Figure 1--- Utilities for First-Order Perceptions Processing ---\n",
        "\n",
        "def get_filtered_files(path):\n",
        "    \"\"\"Return sorted list of Excel files containing 'Actor' in filename.\"\"\"\n",
        "    return sorted([f for f in os.listdir(path) if \"Actor\" in f and f.endswith('.xlsx')])\n",
        "\n",
        "def extract_actor_name(filename):\n",
        "    \"\"\"Extract and lowercase actor name from filename (e.g., 'Actor 3a.xlsx' → 'actor 3a').\"\"\"\n",
        "    return os.path.splitext(filename)[0].strip().lower()\n",
        "\n",
        "def simplify_actor_name(name):\n",
        "    \"\"\"Simplify actor name by removing trailing letters (e.g., 'actor 3a' → 'actor 3').\"\"\"\n",
        "    parts = name.split()\n",
        "    return f\"{parts[0]} {parts[1][:-1]}\" if len(parts) == 2 and parts[1][-1].isalpha() else name\n",
        "\n",
        "def select_row_by_actor_name(df, actor_name):\n",
        "    \"\"\"Return the row from the DataFrame that matches the simplified actor name.\"\"\"\n",
        "    try:\n",
        "        df = df.set_index(df.columns[0])\n",
        "        df.index = df.index.str.strip().str.lower()\n",
        "        return df.loc[actor_name]\n",
        "    except KeyError:\n",
        "        print(f\"⚠️ Actor row '{actor_name}' not found. Index values: {list(df.index)}\")\n",
        "        return None\n",
        "\n",
        "def process_files(file_list, folder):\n",
        "    \"\"\"Process Excel files: extract rows where filename matches a row index.\"\"\"\n",
        "    selected, labels = [], []\n",
        "    for file in file_list:\n",
        "        try:\n",
        "            df = pd.read_excel(os.path.join(folder, file))\n",
        "            actor_raw = extract_actor_name(file)\n",
        "            actor = simplify_actor_name(actor_raw)\n",
        "            row = select_row_by_actor_name(df, actor)\n",
        "            if row is not None:\n",
        "                selected.append(row)\n",
        "                labels.append(actor_raw.title())\n",
        "        except Exception as e:\n",
        "            print(f\"❌ Failed to process {file}: {e}\")\n",
        "    return pd.DataFrame(selected, index=labels)\n",
        "\n",
        "def extract_before_after_columns(df):\n",
        "    \"\"\"Split DataFrame into 'before' and 'after' perception columns.\"\"\"\n",
        "    before = df.loc[:, df.columns.str.contains(\"before\", case=False)]\n",
        "    after  = df.loc[:, df.columns.str.contains(\"after\", case=False)]\n",
        "    return before, after\n",
        "\n",
        "# --- Run Processing ---\n",
        "\n",
        "# Get Excel file lists\n",
        "list_cleaned_savognin = get_filtered_files('/content/Savognin/')\n",
        "list_cleaned_sedrun   = get_filtered_files('/content/Sedrun/')\n",
        "\n",
        "# Process each region's Excel files\n",
        "savognin_df = process_files(list_cleaned_savognin, '/content/Savognin')\n",
        "sedrun_df   = process_files(list_cleaned_sedrun, '/content/Sedrun')\n",
        "\n",
        "# Extract before/after data\n",
        "savognin_before, savognin_after = extract_before_after_columns(savognin_df)\n",
        "sedrun_before, sedrun_after     = extract_before_after_columns(sedrun_df)\n",
        "\n",
        "# --- Display Results ---\n",
        "\n",
        "print(\"📌 Savognin – Before Process:\")\n",
        "display(savognin_before)\n",
        "print(\"📌 Savognin – After Process:\")\n",
        "display(savognin_after)\n",
        "\n",
        "print(\"📌 Sedrun – Before Process:\")\n",
        "display(sedrun_before)\n",
        "print(\"📌 Sedrun – After Process:\")\n",
        "display(sedrun_after)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "AJREExwBIzLM",
        "outputId": "297329e7-2122-444b-a419-594e242043d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "📌 Savognin – Before Process:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          Biodiversity (before process)  Landscape (before process)  \\\n",
              "Actor 1                             5.0                         1.0   \n",
              "Actor 2                             1.0                         1.0   \n",
              "Actor 3A                            1.0                         1.0   \n",
              "Actor 3B                            5.0                         5.0   \n",
              "Actor 3C                            1.0                         1.0   \n",
              "Actor 4                             5.0                         1.0   \n",
              "Actor 6                             5.0                         5.0   \n",
              "Actor 8                             0.0                         1.0   \n",
              "\n",
              "          Agriculture (before process)  Hydropower (before process)  \\\n",
              "Actor 1                            5.0                          5.0   \n",
              "Actor 2                            1.0                          NaN   \n",
              "Actor 3A                           1.0                          5.0   \n",
              "Actor 3B                           1.0                          NaN   \n",
              "Actor 3C                           1.0                          NaN   \n",
              "Actor 4                            5.0                          5.0   \n",
              "Actor 6                            5.0                          NaN   \n",
              "Actor 8                            0.0                          5.0   \n",
              "\n",
              "          Energy security (before process)  Finances (before process)  \\\n",
              "Actor 1                                5.0                        5.0   \n",
              "Actor 2                                5.0                        0.0   \n",
              "Actor 3A                               5.0                        1.0   \n",
              "Actor 3B                               0.0                        1.0   \n",
              "Actor 3C                               1.0                        5.0   \n",
              "Actor 4                                0.0                        0.0   \n",
              "Actor 6                                0.0                        0.0   \n",
              "Actor 8                                0.0                        0.0   \n",
              "\n",
              "          Employment (before process)  \\\n",
              "Actor 1                           5.0   \n",
              "Actor 2                           5.0   \n",
              "Actor 3A                          5.0   \n",
              "Actor 3B                          5.0   \n",
              "Actor 3C                          5.0   \n",
              "Actor 4                           0.0   \n",
              "Actor 6                           0.0   \n",
              "Actor 8                           0.0   \n",
              "\n",
              "          Status Quo of power relations (before process)  \n",
              "Actor 1                                              NaN  \n",
              "Actor 2                                              NaN  \n",
              "Actor 3A                                             5.0  \n",
              "Actor 3B                                             1.0  \n",
              "Actor 3C                                             1.0  \n",
              "Actor 4                                              5.0  \n",
              "Actor 6                                              NaN  \n",
              "Actor 8                                              0.0  "
            ],
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Biodiversity (before process)</th>\n",
              "      <th>Landscape (before process)</th>\n",
              "      <th>Agriculture (before process)</th>\n",
              "      <th>Hydropower (before process)</th>\n",
              "      <th>Energy security (before process)</th>\n",
              "      <th>Finances (before process)</th>\n",
              "      <th>Employment (before process)</th>\n",
              "      <th>Status Quo of power relations (before process)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Actor 1</th>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actor 2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actor 3A</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actor 3B</th>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actor 3C</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actor 4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actor 6</th>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actor 8</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2566bd4b-b95c-4df4-a9fe-522e8b9f9d23')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
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              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        (() => {\n",
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              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('savognin_before')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "savognin_before",
              "summary": "{\n  \"name\": \"savognin_before\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Biodiversity (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.295181287579947,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.0,\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Landscape (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8516401995451028,\n        \"min\": 1.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Agriculture (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1998376563477846,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hydropower (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 5.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Energy security (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.5071326821120348,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Finances (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.2038926600773587,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Employment (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.5877458475338284,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Status Quo of power relations (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4083189157584592,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "📌 Savognin – After Process:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          Biodiversity (after process)   Landscape (after process)   \\\n",
              "Actor 1                               5                           1   \n",
              "Actor 2                               1                           1   \n",
              "Actor 3A                              1                           1   \n",
              "Actor 3B                              1                           1   \n",
              "Actor 3C                              1                           1   \n",
              "Actor 4                               5                           1   \n",
              "Actor 6                               5                           5   \n",
              "Actor 8                               0                           1   \n",
              "\n",
              "          Agriculture (after process)  Hydropower (after process)   \\\n",
              "Actor 1                              5                           5   \n",
              "Actor 2                              1                         NA    \n",
              "Actor 3A                             1                           5   \n",
              "Actor 3B                             1                         NA    \n",
              "Actor 3C                             1                         NaN   \n",
              "Actor 4                              5                           5   \n",
              "Actor 6                              5                         NaN   \n",
              "Actor 8                              0                           5   \n",
              "\n",
              "          Energy security (after process)   Finances (after process)   \\\n",
              "Actor 1                                  5                          5   \n",
              "Actor 2                                  5                          0   \n",
              "Actor 3A                                 5                          1   \n",
              "Actor 3B                                 0                          1   \n",
              "Actor 3C                                 1                          5   \n",
              "Actor 4                                  0                          0   \n",
              "Actor 6                                  0                          0   \n",
              "Actor 8                                  0                          0   \n",
              "\n",
              "          Employment (after process)   \\\n",
              "Actor 1                             5   \n",
              "Actor 2                             5   \n",
              "Actor 3A                            5   \n",
              "Actor 3B                            5   \n",
              "Actor 3C                            5   \n",
              "Actor 4                             0   \n",
              "Actor 6                             0   \n",
              "Actor 8                             0   \n",
              "\n",
              "         Status Quo of power relations (after process)   \n",
              "Actor 1                                             NA   \n",
              "Actor 2                                             NA   \n",
              "Actor 3A                                              5  \n",
              "Actor 3B                                              1  \n",
              "Actor 3C                                              1  \n",
              "Actor 4                                               5  \n",
              "Actor 6                                             NaN  \n",
              "Actor 8                                               0  "
            ],
            "text/html": [
              "\n",
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              "      <th>Biodiversity (after process)</th>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "\n",
              "\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-50929bff-dfe0-4a45-8566-fe9196e846c9')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
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              "      </button>\n",
              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    padding: 0;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        (() => {\n",
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              "            document.querySelector('#df-50929bff-dfe0-4a45-8566-fe9196e846c9 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      </script>\n",
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              "\n",
              "  <div id=\"id_6118cd30-9009-4634-a330-dd18d40dd415\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('savognin_after')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('savognin_after');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "savognin_after",
              "summary": "{\n  \"name\": \"savognin_after\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Biodiversity (after process) \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Landscape (after process) \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Agriculture (after process) \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hydropower (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"NA \",\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Energy security (after process) \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Finances (after process) \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Employment (after process) \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Status Quo of power relations (after process) \",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "📌 Sedrun – Before Process:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "         Biodiversity (before process)  Landscape (before process)  \\\n",
              "Actor 1                            5.0                         5.0   \n",
              "Actor 3                            5.0                         5.0   \n",
              "Actor 4                            5.0                         1.0   \n",
              "Actor 5                            5.0                         1.0   \n",
              "\n",
              "         Agriculture (before process)  Hydropower (before process)  \\\n",
              "Actor 1                           5.0                          5.0   \n",
              "Actor 3                           5.0                          NaN   \n",
              "Actor 4                           5.0                          5.0   \n",
              "Actor 5                           5.0                          5.0   \n",
              "\n",
              "         Energy security (before process)  Finances (before process)  \\\n",
              "Actor 1                               0.0                        5.0   \n",
              "Actor 3                               0.0                        5.0   \n",
              "Actor 4                               0.0                        0.0   \n",
              "Actor 5                               0.0                        0.0   \n",
              "\n",
              "         Employment (before process)  \\\n",
              "Actor 1                          0.0   \n",
              "Actor 3                          5.0   \n",
              "Actor 4                          0.0   \n",
              "Actor 5                          0.0   \n",
              "\n",
              "         Status Quo of power relations (before process)  \n",
              "Actor 1                                             NaN  \n",
              "Actor 3                                             5.0  \n",
              "Actor 4                                             0.0  \n",
              "Actor 5                                             0.0  "
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              "      <th>Biodiversity (before process)</th>\n",
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              "        const element = document.querySelector('#df-228918b9-7519-431f-8ca6-df360bd7f411');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-cde333fd-0332-4cfb-bb20-b4cc70b320e8 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_44021289-be01-4a8d-bc15-3d739d2a57a3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('sedrun_before')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_44021289-be01-4a8d-bc15-3d739d2a57a3 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('sedrun_before');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sedrun_before",
              "summary": "{\n  \"name\": \"sedrun_before\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Biodiversity (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 5.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Landscape (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.309401076758503,\n        \"min\": 1.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Agriculture (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 5.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hydropower (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 5.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Energy security (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Finances (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.886751345948129,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Employment (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.5,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Status Quo of power relations (before process)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8867513459481287,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "📌 Sedrun – After Process:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "         Biodiversity (after process)   Landscape (after process)   \\\n",
              "Actor 1                            5.0                         5.0   \n",
              "Actor 3                            0.0                         5.0   \n",
              "Actor 4                            0.0                         1.0   \n",
              "Actor 5                            0.0                         1.0   \n",
              "\n",
              "         Agriculture (after process)  Hydropower (after process)   \\\n",
              "Actor 1                           0.0                           5   \n",
              "Actor 3                           5.0                         NA    \n",
              "Actor 4                           0.0                           5   \n",
              "Actor 5                           5.0                           5   \n",
              "\n",
              "         Energy security (after process)   Finances (after process)   \\\n",
              "Actor 1                               0.0                        5.0   \n",
              "Actor 3                               0.0                        5.0   \n",
              "Actor 4                               0.0                        0.0   \n",
              "Actor 5                               0.0                        0.0   \n",
              "\n",
              "         Employment (after process)   \\\n",
              "Actor 1                          0.0   \n",
              "Actor 3                          5.0   \n",
              "Actor 4                          0.0   \n",
              "Actor 5                          0.0   \n",
              "\n",
              "        Status Quo of power relations (after process)   \n",
              "Actor 1                                            NA   \n",
              "Actor 3                                            5.0  \n",
              "Actor 4                                              0  \n",
              "Actor 5                                              0  "
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Biodiversity (after process)</th>\n",
              "      <th>Landscape (after process)</th>\n",
              "      <th>Agriculture (after process)</th>\n",
              "      <th>Hydropower (after process)</th>\n",
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              "      <th>Actor 1</th>\n",
              "      <td>5.0</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-365f52cd-5ea3-4a0d-9cea-839c38951c6f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-365f52cd-5ea3-4a0d-9cea-839c38951c6f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "# Figure 1, Network analysis First order perceptions\n",
        "\n",
        "# --- Helper functions ---\n",
        "def clean_topic_names(topic_names):\n",
        "    return [name.replace(\" (before process)\", \"\").replace(\" (after process)\", \"\") for name in topic_names]\n",
        "\n",
        "def create_network(incidence_matrix, actor_names, topic_names, ax):\n",
        "    n_actors = len(actor_names)\n",
        "    n_topics = len(topic_names)\n",
        "    cleaned_topic_names = clean_topic_names(topic_names)\n",
        "\n",
        "    g = ig.Graph(directed=False)\n",
        "    g.add_vertices(n_actors + n_topics)\n",
        "\n",
        "    # Set vertex names (actors as \"Actor 1\", \"Actor 2\", etc.)\n",
        "    actor_ids = [f\"Actor {i + 1}\" for i in range(n_actors)]\n",
        "    g.vs[\"name\"] = actor_ids + cleaned_topic_names\n",
        "\n",
        "    # Add edges\n",
        "    for i in range(n_actors):\n",
        "        for j in range(n_topics):\n",
        "            val = incidence_matrix[i, j]\n",
        "            if val in [0, 1]:\n",
        "                g.add_edge(i, n_actors + j)\n",
        "\n",
        "    # Edge colors\n",
        "    edge_colors = []\n",
        "    for i in range(n_actors):\n",
        "        for j in range(n_topics):\n",
        "            val = incidence_matrix[i, j]\n",
        "            if val == 1:\n",
        "                edge_colors.append('#d62728')  # Conflict\n",
        "            elif val == 0:\n",
        "                edge_colors.append('#1f77b4')  # Synergy\n",
        "\n",
        "    # Node sizes\n",
        "    actor_size = 40\n",
        "    topic_tradeoffs = np.sum(incidence_matrix == 1, axis=0)\n",
        "    normalized_tradeoffs = topic_tradeoffs / np.max(topic_tradeoffs) if np.max(topic_tradeoffs) > 0 else topic_tradeoffs\n",
        "    topic_sizes = 40 + normalized_tradeoffs * 40\n",
        "    node_sizes = [actor_size] * n_actors + topic_sizes.tolist()\n",
        "    node_colors = ['lavender'] * n_actors + ['lightgreen'] * n_topics\n",
        "\n",
        "    # Layout\n",
        "    layout = g.layout_fruchterman_reingold()\n",
        "    layout_coords = np.array(layout.coords)\n",
        "    # layout_coords += np.random.normal(scale=5, size=layout_coords.shape)\n",
        "    layout_coords *= max(node_sizes) * 1.5\n",
        "\n",
        "# Kein zusätzliches Rauschen oder Skalieren mehr nötig\n",
        "\n",
        "\n",
        "    # Normalize to 1000x1000 space\n",
        "    x_coords, y_coords = layout_coords[:, 0], layout_coords[:, 1]\n",
        "    x_min, x_max = x_coords.min(), x_coords.max()\n",
        "    y_min, y_max = y_coords.min(), y_coords.max()\n",
        "    margin = 50\n",
        "    x_range = x_max - x_min\n",
        "    y_range = y_max - y_min\n",
        "    layout_coords[:, 0] = ((x_coords - x_min) / x_range) * (1000 - 2 * margin) + margin\n",
        "    layout_coords[:, 1] = ((y_coords - y_min) / y_range) * (1000 - 2 * margin) + margin\n",
        "    layout_coords *= 1.3  # Optional scale\n",
        "\n",
        "    # Plot\n",
        "    ax.set_aspect(\"equal\")\n",
        "    ax.axis(\"off\")\n",
        "    ig.plot(\n",
        "        g,\n",
        "        layout=layout_coords.tolist(),\n",
        "        target=ax,\n",
        "        vertex_size=node_sizes,\n",
        "        vertex_color=node_colors,\n",
        "        edge_color=edge_colors,\n",
        "        edge_width=2,\n",
        "        bbox=(0, 0, 1000, 1000),\n",
        "        margin=0\n",
        "    )\n",
        "\n",
        "    # Add labels only for topics\n",
        "    for i in range(len(actor_ids), len(g.vs)):\n",
        "        v = g.vs[i]\n",
        "        ax.text(layout_coords[i][0], layout_coords[i][1], v[\"name\"], fontsize=15,\n",
        "              ha='center', va='center')\n",
        "\n",
        "\n",
        "\n",
        "# --- Plot Setup ---\n",
        "fig, axs = plt.subplots(2, 2, figsize=(20, 20))\n",
        "\n",
        "# Each panel data: (df, actor list)\n",
        "data = [\n",
        "    (savognin_before, savognin_df.index),  # (a)\n",
        "    (sedrun_before, sedrun_df.index),     # (b)\n",
        "    (savognin_after, savognin_df.index),  # (c)\n",
        "    (sedrun_after, sedrun_df.index)       # (d)\n",
        "]\n",
        "\n",
        "labels = ['(a)', '(b)', '(c)', '(d)']\n",
        "label_positions = {\n",
        "    '(a)': (0.06, 0.95),\n",
        "    '(b)': (0.54, 0.95),\n",
        "    '(c)': (0.06, 0.48),\n",
        "    '(d)': (0.54, 0.48)\n",
        "}\n",
        "\n",
        "# --- Draw each subplot ---\n",
        "for ax, (df_part, actor_index), label in zip(axs.flat, data, labels):\n",
        "    if df_part is not None:\n",
        "        create_network(df_part.to_numpy(), list(actor_index), df_part.columns, ax)\n",
        "    else:\n",
        "        ax.text(0.5, 0.5, \"No data\", ha='center', fontsize=14)\n",
        "        ax.axis(\"off\")\n",
        "\n",
        "# Add panel labels\n",
        "for label, (x, y) in label_positions.items():\n",
        "    fig.text(x, y, label, fontsize=14, fontweight='bold')\n",
        "\n",
        "# --- Combined Legend ---\n",
        "legend_elements = [\n",
        "    Line2D([0], [0], color='#d62728', lw=5, label='Trade-off'),\n",
        "    Line2D([0], [0], color='#1f77b4', lw=5, label='Synergy'),\n",
        "    Line2D([0], [0], marker='o', color='w', label='Actor', markerfacecolor='lavender', markersize=15),\n",
        "    Line2D([0], [0], marker='o', color='w', label='Topic', markerfacecolor='lightgreen', markersize=15),\n",
        "    Line2D([0], [0], color='none', label=''),\n",
        "    Line2D([0], [0], color='none', label='(a) Savognin – Before Process'),\n",
        "    Line2D([0], [0], color='none', label='(b) Sedrun – Before Process'),\n",
        "    Line2D([0], [0], color='none', label='(c) Savognin – After Process'),\n",
        "    Line2D([0], [0], color='none', label='(d) Sedrun – After Process'),\n",
        "]\n",
        "\n",
        "plt.subplots_adjust(hspace=0.2, wspace=0.2, bottom=0.1, top=0.95)\n",
        "fig.legend(handles=legend_elements, loc='lower center', ncol=2, frameon=False, fontsize=12)\n",
        "\n",
        "plt.savefig(\"Network_analysis_V5.pdf\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        },
        "id": "mCGen4cDSTBT",
        "outputId": "be59e799-6e56-4371-fc56-e172c5624b92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Figure 2, quantitative barcharts (data preparation)\n",
        "\n",
        "def select_rows_not_matching_actor_name(df, actor_name):\n",
        "    actor_col = df.columns[0]\n",
        "    if actor_col not in df.columns:\n",
        "        print(f\"⚠️ Column '{actor_col}' not found in dataframe\")\n",
        "        return None\n",
        "\n",
        "    # Compare lowercase, stripped names\n",
        "    filtered = df[df[actor_col].astype(str).str.lower().str.strip() != actor_name.lower().strip()]\n",
        "    return filtered if not filtered.empty else None\n",
        "\n",
        "\n",
        "def process_non_matching_rows(file_list, base_dir):\n",
        "    selected_rows = []\n",
        "    for file in file_list:\n",
        "        try:\n",
        "            file_path = os.path.join(base_dir, file)\n",
        "            df = pd.read_excel(file_path)\n",
        "\n",
        "            actor_name_raw = extract_actor_name(file)\n",
        "            actor_name = simplify_actor_name(actor_name_raw)\n",
        "\n",
        "            selected_row = select_rows_not_matching_actor_name(df, actor_name)\n",
        "            if selected_row is not None:\n",
        "                selected_rows.append(selected_row)\n",
        "\n",
        "        except Exception as e:\n",
        "            print(f\"⚠️ Failed to process {file}: {e}\")\n",
        "\n",
        "    if selected_rows:\n",
        "        return pd.concat(selected_rows, axis=0, ignore_index=True)\n",
        "    else:\n",
        "        print(\"⚠️ No non-matching rows found.\")\n",
        "        return pd.DataFrame()  # Return empty DataFrame\n",
        "\n",
        "\n",
        "# Define base directories if not yet defined\n",
        "savognin_base_dir = '/content/Savognin'\n",
        "sedrun_base_dir = '/content/Sedrun'\n",
        "\n",
        "# Process both locations\n",
        "first_order_df_not_matching_savognin = process_non_matching_rows(list_cleaned_savognin, savognin_base_dir)\n",
        "first_order_df_not_matching_sedrun   = process_non_matching_rows(list_cleaned_sedrun, sedrun_base_dir)\n",
        "\n",
        "# Display results\n",
        "print(\"✅ Rows not matching actor names for Savognin:\")\n",
        "display(first_order_df_not_matching_savognin)\n",
        "\n",
        "print(\"✅ Rows not matching actor names for Sedrun:\")\n",
        "display(first_order_df_not_matching_sedrun)\n",
        "\n",
        "# Count rows by original DataFrame index\n",
        "actor_row_counts_savognin = first_order_df_not_matching_savognin.groupby(first_order_df_not_matching_savognin.index).size()\n",
        "actor_row_counts_sedrun   = first_order_df_not_matching_sedrun.groupby(first_order_df_not_matching_sedrun.index).size()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GiumcYNXsa0P",
        "outputId": "ba77ef84-0c45-43d2-fbd4-e00735587fa6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Rows not matching actor names for Savognin:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    Actor 1 Biodiversity (before process) Biodiversity (after process)   \\\n",
              "0   Actor 2                           NaN                           NA    \n",
              "1   Actor 3                           NaN                           NA    \n",
              "2   Actor 4                           NaN                           NA    \n",
              "3   Actor 5                           NaN                           NA    \n",
              "4   Actor 6                           NaN                           NA    \n",
              "5   Actor 7                           NaN                           NA    \n",
              "6   Actor 8                           NaN                           NA    \n",
              "7       NaN                           NaN                           NA    \n",
              "8       NaN                           NaN                           NA    \n",
              "9       NaN                           NaN                           NA    \n",
              "10      NaN                           NaN                           NA    \n",
              "11      NaN                           NaN                           NA    \n",
              "12      NaN                           NaN                           NA    \n",
              "13      NaN                           NaN                           NA    \n",
              "14      NaN                           NaN                           NaN   \n",
              "15      NaN                           NaN                           NaN   \n",
              "16      NaN                           NaN                           NaN   \n",
              "17      NaN                           NaN                           NaN   \n",
              "18      NaN                           NaN                           NaN   \n",
              "19      NaN                           NaN                           NA    \n",
              "20      NaN                           NaN                           NA    \n",
              "21      NaN                           NA                            NA    \n",
              "22      NaN                           NA                            NA    \n",
              "23      NaN                           NA                            NaN   \n",
              "24      NaN                           NA                            NaN   \n",
              "25      NaN                           NA                            NaN   \n",
              "26      NaN                           NA                            NaN   \n",
              "27      NaN                           NA                            NaN   \n",
              "28      NaN                             1                             1   \n",
              "29      NaN                           NA                            NA    \n",
              "30      NaN                           NA                            NA    \n",
              "31      NaN                           NA                            NA    \n",
              "32      NaN                           NA                            NA    \n",
              "33      NaN                           NaN                           NaN   \n",
              "34      NaN                           NaN                           NaN   \n",
              "35      NaN                           1.0                             1   \n",
              "36      NaN                           5.0                             5   \n",
              "37      NaN                           1.0                             1   \n",
              "38      NaN                           NaN                           NA    \n",
              "39      NaN                           5.0                             5   \n",
              "40      NaN                           NaN                           NA    \n",
              "41      NaN                           NaN                           NA    \n",
              "42      NaN                           5.0                             5   \n",
              "43      NaN                           1.0                             1   \n",
              "44      NaN                           NaN                           NaN   \n",
              "45      NaN                           NaN                           NaN   \n",
              "46      NaN                           NaN                           NaN   \n",
              "47      NaN                           NaN                           NA    \n",
              "48      NaN                           NaN                           NA    \n",
              "49      NaN                           5.0                             1   \n",
              "50      NaN                           NaN                           NA    \n",
              "51      NaN                           1.0                             1   \n",
              "52      NaN                           NaN                           NaN   \n",
              "53      NaN                           NaN                           NaN   \n",
              "54      NaN                           NaN                           NaN   \n",
              "55      NaN                           NaN                           NaN   \n",
              "\n",
              "   Landscape (before process) Landscape (after process)   \\\n",
              "0                         NaN                        NA    \n",
              "1                         NaN                        NA    \n",
              "2                         NaN                        NA    \n",
              "3                         NaN                        NA    \n",
              "4                         NaN                        NA    \n",
              "5                         NaN                        NA    \n",
              "6                         NaN                        NA    \n",
              "7                         1.0                          1   \n",
              "8                         NaN                        NA    \n",
              "9                         NaN                        NA    \n",
              "10                        NaN                        NA    \n",
              "11                        NaN                        NA    \n",
              "12                        NaN                        NA    \n",
              "13                        NaN                        NA    \n",
              "14                        NaN                        NaN   \n",
              "15                        NaN                        NaN   \n",
              "16                        NaN                        NaN   \n",
              "17                        NaN                        NaN   \n",
              "18                        NaN                        NaN   \n",
              "19                        NaN                        NA    \n",
              "20                        NaN                        NA    \n",
              "21                          1                          1   \n",
              "22                        NA                         NA    \n",
              "23                        NA                         NaN   \n",
              "24                        NA                         NaN   \n",
              "25                        NA                         NaN   \n",
              "26                        NA                         NaN   \n",
              "27                        NA                         NaN   \n",
              "28                          1                          1   \n",
              "29                        NA                         NA    \n",
              "30                        NA                         NA    \n",
              "31                        NA                         NA    \n",
              "32                        NA                         NA    \n",
              "33                        NaN                        NaN   \n",
              "34                        NaN                        NaN   \n",
              "35                        1.0                          1   \n",
              "36                        5.0                          5   \n",
              "37                        5.0                          1   \n",
              "38                        NaN                        NA    \n",
              "39                        5.0                          5   \n",
              "40                        NaN                        NA    \n",
              "41                        NaN                        NA    \n",
              "42                        1.0                          1   \n",
              "43                        1.0                          1   \n",
              "44                        NaN                        NaN   \n",
              "45                        NaN                        NaN   \n",
              "46                        NaN                        NaN   \n",
              "47                        NaN                        NA    \n",
              "48                        NaN                        NA    \n",
              "49                        1.0                          1   \n",
              "50                        NaN                        NA    \n",
              "51                        5.0                          1   \n",
              "52                        NaN                        NaN   \n",
              "53                        NaN                        NaN   \n",
              "54                        NaN                        NaN   \n",
              "55                        NaN                        NaN   \n",
              "\n",
              "   Agriculture (before process) Agriculture (after process)   \\\n",
              "0                           NaN                          NA    \n",
              "1                           NaN                          NA    \n",
              "2                           NaN                          NA    \n",
              "3                           NaN                          NA    \n",
              "4                           NaN                          NA    \n",
              "5                           NaN                          NA    \n",
              "6                           NaN                          NA    \n",
              "7                           NaN                          NA    \n",
              "8                           NaN                          NA    \n",
              "9                           NaN                          NA    \n",
              "10                          NaN                          NA    \n",
              "11                          NaN                          NA    \n",
              "12                          NaN                          NA    \n",
              "13                          NaN                          NA    \n",
              "14                          NaN                          NaN   \n",
              "15                          NaN                          NaN   \n",
              "16                          NaN                          NaN   \n",
              "17                          NaN                          NaN   \n",
              "18                          NaN                          NaN   \n",
              "19                          NaN                          NA    \n",
              "20                          NaN                          NA    \n",
              "21                          NaN                          NA    \n",
              "22                            1                            1   \n",
              "23                          NA                           NaN   \n",
              "24                          NA                           NaN   \n",
              "25                          NA                           NaN   \n",
              "26                          NA                           NaN   \n",
              "27                          NA                           NaN   \n",
              "28                          NA                           NA    \n",
              "29                          NA                           NA    \n",
              "30                          NA                           NA    \n",
              "31                          NA                           NA    \n",
              "32                          NA                           NA    \n",
              "33                          NaN                          NaN   \n",
              "34                          NaN                          NaN   \n",
              "35                          5.0                            5   \n",
              "36                          1.0                            1   \n",
              "37                          5.0                            1   \n",
              "38                          NaN                          NA    \n",
              "39                          5.0                            5   \n",
              "40                          NaN                          NA    \n",
              "41                          NaN                          NA    \n",
              "42                          5.0                            5   \n",
              "43                          1.0                            1   \n",
              "44                          NaN                          NaN   \n",
              "45                          NaN                          NaN   \n",
              "46                          NaN                          NaN   \n",
              "47                          NaN                          NA    \n",
              "48                          NaN                          NA    \n",
              "49                          NA                           NA    \n",
              "50                          NaN                          NaN   \n",
              "51                          NaN                          NA    \n",
              "52                          NaN                          NaN   \n",
              "53                          NaN                          NaN   \n",
              "54                          NaN                          NaN   \n",
              "55                          NaN                          NaN   \n",
              "\n",
              "   Hydropower (before process) Hydropower (after process)   \\\n",
              "0                          NaN                         NA    \n",
              "1                          NaN                         NA    \n",
              "2                          NaN                         NA    \n",
              "3                          NaN                         NA    \n",
              "4                          NaN                         NA    \n",
              "5                          NaN                         NA    \n",
              "6                          NaN                         NA    \n",
              "7                          NaN                         NA    \n",
              "8                          NaN                         NA    \n",
              "9                          NaN                         NA    \n",
              "10                         NaN                         NA    \n",
              "11                         NaN                         NA    \n",
              "12                         NaN                         NA    \n",
              "13                         NaN                         NA    \n",
              "14                         NaN                         NaN   \n",
              "15                         NaN                         NaN   \n",
              "16                         NaN                         NaN   \n",
              "17                         NaN                         NaN   \n",
              "18                         NaN                         NaN   \n",
              "19                         NaN                         NA    \n",
              "20                         NaN                         NA    \n",
              "21                         NaN                         NA    \n",
              "22                         NA                          NA    \n",
              "23                         NA                          NaN   \n",
              "24                         NA                          NaN   \n",
              "25                         NA                          NaN   \n",
              "26                         NA                          NaN   \n",
              "27                         NA                          NaN   \n",
              "28                         NA                          NA    \n",
              "29                         NA                          NA    \n",
              "30                         NA                          NA    \n",
              "31                         NA                          NA    \n",
              "32                         NA                          NA    \n",
              "33                         NaN                         NaN   \n",
              "34                         NaN                         NaN   \n",
              "35                         5.0                           5   \n",
              "36                         5.0                           5   \n",
              "37                         5.0                           5   \n",
              "38                         NaN                         NA    \n",
              "39                         5.0                           5   \n",
              "40                         NaN                         NA    \n",
              "41                         NaN                         NA    \n",
              "42                         NaN                         NA    \n",
              "43                         NaN                         NA    \n",
              "44                         NaN                         NaN   \n",
              "45                         NaN                         NaN   \n",
              "46                         NaN                         NaN   \n",
              "47                         NaN                         NA    \n",
              "48                         NaN                         NA    \n",
              "49                         NaN                         NA    \n",
              "50                         NaN                         NA    \n",
              "51                         NaN                         NA    \n",
              "52                         NaN                         NaN   \n",
              "53                         NaN                         NaN   \n",
              "54                         NaN                         NaN   \n",
              "55                         NaN                         NaN   \n",
              "\n",
              "   Energy security (before process)  ... Employment (after process)   \\\n",
              "0                               NaN  ...                         NA    \n",
              "1                               NaN  ...                         NA    \n",
              "2                               NaN  ...                         NA    \n",
              "3                               NaN  ...                         NA    \n",
              "4                               NaN  ...                         NA    \n",
              "5                               NaN  ...                         NA    \n",
              "6                               NaN  ...                         NA    \n",
              "7                               NaN  ...                         NA    \n",
              "8                               NaN  ...                         NA    \n",
              "9                               NaN  ...                         NA    \n",
              "10                              NaN  ...                         NA    \n",
              "11                              NaN  ...                         NA    \n",
              "12                              NaN  ...                         NA    \n",
              "13                              NaN  ...                         NA    \n",
              "14                              NaN  ...                         NaN   \n",
              "15                              NaN  ...                         NaN   \n",
              "16                              NaN  ...                         NaN   \n",
              "17                              NaN  ...                         NaN   \n",
              "18                              NaN  ...                         NaN   \n",
              "19                              NaN  ...                         NA    \n",
              "20                              NaN  ...                         NA    \n",
              "21                              NaN  ...                         NA    \n",
              "22                              NA   ...                         NA    \n",
              "23                              NA   ...                         NaN   \n",
              "24                              NA   ...                         NaN   \n",
              "25                              NA   ...                         NaN   \n",
              "26                              NA   ...                         NaN   \n",
              "27                              NA   ...                         NaN   \n",
              "28                              NA   ...                         NA    \n",
              "29                              NA   ...                         NA    \n",
              "30                              NA   ...                           0   \n",
              "31                              NA   ...                         NA    \n",
              "32                              NA   ...                         NA    \n",
              "33                              NaN  ...                         NaN   \n",
              "34                              NaN  ...                         NaN   \n",
              "35                              0.0  ...                           5   \n",
              "36                              0.0  ...                           5   \n",
              "37                              0.0  ...                           5   \n",
              "38                              NaN  ...                         NA    \n",
              "39                              0.0  ...                           5   \n",
              "40                              NaN  ...                         NA    \n",
              "41                              NaN  ...                         NA    \n",
              "42                              5.0  ...                         NA    \n",
              "43                              NaN  ...                         NA    \n",
              "44                              NaN  ...                         NaN   \n",
              "45                              NaN  ...                         NaN   \n",
              "46                              NaN  ...                         NaN   \n",
              "47                              NaN  ...                         NA    \n",
              "48                              NaN  ...                         NA    \n",
              "49                              NaN  ...                         NA    \n",
              "50                              NaN  ...                         NA    \n",
              "51                              NaN  ...                         NA    \n",
              "52                              NaN  ...                         NA    \n",
              "53                              NaN  ...                         NaN   \n",
              "54                              NaN  ...                         NaN   \n",
              "55                              NaN  ...                         NaN   \n",
              "\n",
              "   Status Quo of power relations (before process)  \\\n",
              "0                                             NaN   \n",
              "1                                             NaN   \n",
              "2                                             NaN   \n",
              "3                                             NaN   \n",
              "4                                             NaN   \n",
              "5                                             NaN   \n",
              "6                                             NaN   \n",
              "7                                             NaN   \n",
              "8                                             NaN   \n",
              "9                                             NaN   \n",
              "10                                            NaN   \n",
              "11                                            NaN   \n",
              "12                                            NaN   \n",
              "13                                            NaN   \n",
              "14                                            NaN   \n",
              "15                                            NaN   \n",
              "16                                            NaN   \n",
              "17                                            NaN   \n",
              "18                                            NaN   \n",
              "19                                            NaN   \n",
              "20                                            NaN   \n",
              "21                                            NaN   \n",
              "22                                            NA    \n",
              "23                                            NA    \n",
              "24                                            NA    \n",
              "25                                            NA    \n",
              "26                                            NA    \n",
              "27                                            NA    \n",
              "28                                            NA    \n",
              "29                                            NA    \n",
              "30                                            NA    \n",
              "31                                            NA    \n",
              "32                                            NA    \n",
              "33                                            NaN   \n",
              "34                                            NaN   \n",
              "35                                            NaN   \n",
              "36                                            NaN   \n",
              "37                                            NaN   \n",
              "38                                            NaN   \n",
              "39                                            NaN   \n",
              "40                                            NaN   \n",
              "41                                            NaN   \n",
              "42                                            NaN   \n",
              "43                                            NaN   \n",
              "44                                            NaN   \n",
              "45                                            NaN   \n",
              "46                                            NaN   \n",
              "47                                            NaN   \n",
              "48                                            NaN   \n",
              "49                                            NaN   \n",
              "50                                            NaN   \n",
              "51                                            NaN   \n",
              "52                                            NaN   \n",
              "53                                            NaN   \n",
              "54                                            NaN   \n",
              "55                                            NaN   \n",
              "\n",
              "   Status Quo of power relations (after process)   Actor 2 Actor 3_a  \\\n",
              "0                                             NA       NaN       NaN   \n",
              "1                                             NA       NaN       NaN   \n",
              "2                                             NA       NaN       NaN   \n",
              "3                                             NA       NaN       NaN   \n",
              "4                                             NA       NaN       NaN   \n",
              "5                                             NA       NaN       NaN   \n",
              "6                                             NA       NaN       NaN   \n",
              "7                                             NA   Actor 1       NaN   \n",
              "8                                             NA   Actor 3       NaN   \n",
              "9                                             NA   Actor 4       NaN   \n",
              "10                                            NA   Actor 5       NaN   \n",
              "11                                            NA   Actor 6       NaN   \n",
              "12                                            NA   Actor 7       NaN   \n",
              "13                                            NA   Actor 8       NaN   \n",
              "14                                            NaN      NaN   Actor 1   \n",
              "15                                            NaN      NaN   Actor 2   \n",
              "16                                            NaN      NaN   Actor 4   \n",
              "17                                            NaN      NaN   Actor 5   \n",
              "18                                            NaN      NaN   Actor 6   \n",
              "19                                            NA       NaN   Actor 7   \n",
              "20                                            NA       NaN   Actor 8   \n",
              "21                                            NA       NaN       NaN   \n",
              "22                                            NA       NaN       NaN   \n",
              "23                                            NaN      NaN       NaN   \n",
              "24                                            NaN      NaN       NaN   \n",
              "25                                            NaN      NaN       NaN   \n",
              "26                                            NaN      NaN       NaN   \n",
              "27                                            NaN      NaN       NaN   \n",
              "28                                            NA       NaN       NaN   \n",
              "29                                            NA       NaN       NaN   \n",
              "30                                            NA       NaN       NaN   \n",
              "31                                            NA       NaN       NaN   \n",
              "32                                            NA       NaN       NaN   \n",
              "33                                            NaN      NaN       NaN   \n",
              "34                                            NaN      NaN       NaN   \n",
              "35                                            NA       NaN       NaN   \n",
              "36                                            NA       NaN       NaN   \n",
              "37                                            NaN      NaN       NaN   \n",
              "38                                            NA       NaN       NaN   \n",
              "39                                            NaN      NaN       NaN   \n",
              "40                                            NA       NaN       NaN   \n",
              "41                                            NA       NaN       NaN   \n",
              "42                                            NA       NaN       NaN   \n",
              "43                                            NA       NaN       NaN   \n",
              "44                                            NaN      NaN       NaN   \n",
              "45                                            NaN      NaN       NaN   \n",
              "46                                            NaN      NaN       NaN   \n",
              "47                                            NA       NaN       NaN   \n",
              "48                                            NA       NaN       NaN   \n",
              "49                                            NA       NaN       NaN   \n",
              "50                                            NA       NaN       NaN   \n",
              "51                                            NA       NaN       NaN   \n",
              "52                                            NA       NaN       NaN   \n",
              "53                                            NaN      NaN       NaN   \n",
              "54                                            NaN      NaN       NaN   \n",
              "55                                            NaN      NaN       NaN   \n",
              "\n",
              "   Actor 3_b Actor 3_c  Actor 4  Actor 6  Actor 8  \n",
              "0        NaN       NaN      NaN      NaN      NaN  \n",
              "1        NaN       NaN      NaN      NaN      NaN  \n",
              "2        NaN       NaN      NaN      NaN      NaN  \n",
              "3        NaN       NaN      NaN      NaN      NaN  \n",
              "4        NaN       NaN      NaN      NaN      NaN  \n",
              "5        NaN       NaN      NaN      NaN      NaN  \n",
              "6        NaN       NaN      NaN      NaN      NaN  \n",
              "7        NaN       NaN      NaN      NaN      NaN  \n",
              "8        NaN       NaN      NaN      NaN      NaN  \n",
              "9        NaN       NaN      NaN      NaN      NaN  \n",
              "10       NaN       NaN      NaN      NaN      NaN  \n",
              "11       NaN       NaN      NaN      NaN      NaN  \n",
              "12       NaN       NaN      NaN      NaN      NaN  \n",
              "13       NaN       NaN      NaN      NaN      NaN  \n",
              "14       NaN       NaN      NaN      NaN      NaN  \n",
              "15       NaN       NaN      NaN      NaN      NaN  \n",
              "16       NaN       NaN      NaN      NaN      NaN  \n",
              "17       NaN       NaN      NaN      NaN      NaN  \n",
              "18       NaN       NaN      NaN      NaN      NaN  \n",
              "19       NaN       NaN      NaN      NaN      NaN  \n",
              "20       NaN       NaN      NaN      NaN      NaN  \n",
              "21   Actor 1       NaN      NaN      NaN      NaN  \n",
              "22   Actor 2       NaN      NaN      NaN      NaN  \n",
              "23   Actor 4       NaN      NaN      NaN      NaN  \n",
              "24   Actor 5       NaN      NaN      NaN      NaN  \n",
              "25   Actor 6       NaN      NaN      NaN      NaN  \n",
              "26   Actor 7       NaN      NaN      NaN      NaN  \n",
              "27   Actor 8       NaN      NaN      NaN      NaN  \n",
              "28       NaN   Actor 1      NaN      NaN      NaN  \n",
              "29       NaN   Actor 2      NaN      NaN      NaN  \n",
              "30       NaN   Actor 4      NaN      NaN      NaN  \n",
              "31       NaN   Actor 5      NaN      NaN      NaN  \n",
              "32       NaN   Actor 6      NaN      NaN      NaN  \n",
              "33       NaN   Actor 7      NaN      NaN      NaN  \n",
              "34       NaN   Actor 8      NaN      NaN      NaN  \n",
              "35       NaN       NaN  Actor 1      NaN      NaN  \n",
              "36       NaN       NaN  Actor 2      NaN      NaN  \n",
              "37       NaN       NaN  Actor 3      NaN      NaN  \n",
              "38       NaN       NaN  Actor 5      NaN      NaN  \n",
              "39       NaN       NaN  Actor 6      NaN      NaN  \n",
              "40       NaN       NaN  Actor 7      NaN      NaN  \n",
              "41       NaN       NaN  Actor 8      NaN      NaN  \n",
              "42       NaN       NaN      NaN  Actor 1      NaN  \n",
              "43       NaN       NaN      NaN  Actor 2      NaN  \n",
              "44       NaN       NaN      NaN  Actor 3      NaN  \n",
              "45       NaN       NaN      NaN  Actor 4      NaN  \n",
              "46       NaN       NaN      NaN  Actor 5      NaN  \n",
              "47       NaN       NaN      NaN  Actor 7      NaN  \n",
              "48       NaN       NaN      NaN  Actor 8      NaN  \n",
              "49       NaN       NaN      NaN      NaN  Actor 1  \n",
              "50       NaN       NaN      NaN      NaN  Actor 2  \n",
              "51       NaN       NaN      NaN      NaN  Actor 3  \n",
              "52       NaN       NaN      NaN      NaN  Actor 4  \n",
              "53       NaN       NaN      NaN      NaN  Actor 5  \n",
              "54       NaN       NaN      NaN      NaN  Actor 6  \n",
              "55       NaN       NaN      NaN      NaN  Actor 7  \n",
              "\n",
              "[56 rows x 24 columns]"
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              "      <th>Actor 1</th>\n",
              "      <th>Biodiversity (before process)</th>\n",
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              "      <th>Landscape (after process)</th>\n",
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              "      <th>Actor 3_b</th>\n",
              "      <th>Actor 3_c</th>\n",
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              "      <th>Actor 6</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Actor 4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Actor 5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Actor 6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Actor 7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Actor 8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>Actor 1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>Actor 3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>Actor 4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>Actor 5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>Actor 6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>Actor 7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>Actor 8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>5.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 2</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 7</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 8</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>56 rows × 24 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17877894-70fe-4188-a520-1e3c3c070021')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-17877894-70fe-4188-a520-1e3c3c070021 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-17877894-70fe-4188-a520-1e3c3c070021');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-9ef030d8-ad54-4564-bb58-ca0b1ac5ef48\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9ef030d8-ad54-4564-bb58-ca0b1ac5ef48')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-9ef030d8-ad54-4564-bb58-ca0b1ac5ef48 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_ebf86677-e15a-463e-9d14-859efd2c8698\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('first_order_df_not_matching_savognin')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('first_order_df_not_matching_savognin');\n",
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              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "first_order_df_not_matching_savognin"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Rows not matching actor names for Sedrun:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    Actor 1 Biodiversity (before process) Biodiversity (after process)   \\\n",
              "0   Actor 2                           NaN                           NaN   \n",
              "1   Actor 3                           NaN                           NA    \n",
              "2   Actor 4                           NaN                           NA    \n",
              "3   Actor 5                           NaN                           NA    \n",
              "4   Actor 6                           NaN                           NaN   \n",
              "5   Actor 7                           NaN                           NA    \n",
              "6   Actor 8                           NaN                           NA    \n",
              "7       NaN                           NaN                           NaN   \n",
              "8       NaN                           5.0                           5.0   \n",
              "9       NaN                           NaN                           NaN   \n",
              "10      NaN                           NaN                           NaN   \n",
              "11      NaN                           NaN                           NaN   \n",
              "12      NaN                           NaN                           NaN   \n",
              "13      NaN                           NaN                           NaN   \n",
              "14      NaN                             5                             5   \n",
              "15      NaN                             5                             5   \n",
              "16      NaN                             1                             5   \n",
              "17      NaN                           NA                            NA    \n",
              "18      NaN                           NA                            NA    \n",
              "19      NaN                           NA                            NA    \n",
              "20      NaN                           NA                            NA    \n",
              "21      NaN                           NA                            NA    \n",
              "22      NaN                           NA                            NaN   \n",
              "23      NaN                             5                             5   \n",
              "24      NaN                           NA                            NaN   \n",
              "25      NaN                           NA                            NaN   \n",
              "26      NaN                           NaN                           NA    \n",
              "27      NaN                           NaN                           NA    \n",
              "\n",
              "   Landscape (before process) Landscape (after process)   \\\n",
              "0                         NaN                        NaN   \n",
              "1                         NaN                        NA    \n",
              "2                         NaN                        NA    \n",
              "3                         NaN                        NA    \n",
              "4                         NaN                        NaN   \n",
              "5                         NaN                        NA    \n",
              "6                         NaN                        NA    \n",
              "7                         NaN                        NaN   \n",
              "8                         5.0                        5.0   \n",
              "9                         NaN                        NaN   \n",
              "10                        NaN                        NaN   \n",
              "11                        NaN                        NaN   \n",
              "12                        NaN                        NaN   \n",
              "13                        NaN                        NaN   \n",
              "14                          1                          1   \n",
              "15                          5                          5   \n",
              "16                          1                          1   \n",
              "17                        NA                         NA    \n",
              "18                        NA                         NA    \n",
              "19                        NA                         NA    \n",
              "20                        NA                         NA    \n",
              "21                        1.0                          1   \n",
              "22                        1.0                          1   \n",
              "23                        1.0                          1   \n",
              "24                        1.0                          1   \n",
              "25                        1.0                          1   \n",
              "26                        NaN                        NA    \n",
              "27                        NaN                        NA    \n",
              "\n",
              "   Agriculture (before process) Agriculture (after process)   \\\n",
              "0                           1.0                            5   \n",
              "1                           NaN                          NA    \n",
              "2                           NaN                          NA    \n",
              "3                           NaN                          NA    \n",
              "4                           NaN                          NaN   \n",
              "5                           NaN                          NA    \n",
              "6                           NaN                          NA    \n",
              "7                           NaN                          NaN   \n",
              "8                           5.0                          5.0   \n",
              "9                           NaN                          NaN   \n",
              "10                          NaN                          NaN   \n",
              "11                          NaN                          NaN   \n",
              "12                          NaN                          NaN   \n",
              "13                          5.0                          5.0   \n",
              "14                            5                            5   \n",
              "15                            5                            0   \n",
              "16                          NA                           NA    \n",
              "17                          NA                           NA    \n",
              "18                          NA                           NA    \n",
              "19                          NA                           NA    \n",
              "20                          NA                           NA    \n",
              "21                          NaN                          NA    \n",
              "22                            5                            0   \n",
              "23                            5                            5   \n",
              "24                          NA                           NaN   \n",
              "25                          NA                           NaN   \n",
              "26                          NaN                          NA    \n",
              "27                          NaN                          NA    \n",
              "\n",
              "   Hydropower (before process) Hydropower (after process)   \\\n",
              "0                          NaN                         NaN   \n",
              "1                          NaN                         NA    \n",
              "2                          NaN                         NA    \n",
              "3                          NaN                         NA    \n",
              "4                          NaN                         NaN   \n",
              "5                          NaN                         NA    \n",
              "6                          NaN                         NA    \n",
              "7                          NaN                         NaN   \n",
              "8                          NaN                         NaN   \n",
              "9                          NaN                         NaN   \n",
              "10                         NaN                         NaN   \n",
              "11                         NaN                         NaN   \n",
              "12                         NaN                         NaN   \n",
              "13                         NaN                         NaN   \n",
              "14                           5                           5   \n",
              "15                           5                           5   \n",
              "16                         NA                          NA    \n",
              "17                         NA                          NA    \n",
              "18                         NA                          NA    \n",
              "19                         NA                          NA    \n",
              "20                         NA                          NA    \n",
              "21                         NaN                         NA    \n",
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              "23                         NA                          NaN   \n",
              "24                         NA                          NaN   \n",
              "25                         NA                          NaN   \n",
              "26                         NaN                         NA    \n",
              "27                         NaN                         NA    \n",
              "\n",
              "   Energy security (before process) Energy security (after process)   \\\n",
              "0                               NaN                              NaN   \n",
              "1                               NaN                              NA    \n",
              "2                               NaN                              NA    \n",
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              "10                              NaN                              NaN   \n",
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              "14                                5                                5   \n",
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              "\n",
              "   Finances (before process) Finances (after process)   \\\n",
              "0                        NaN                       NaN   \n",
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              "8                        NaN                       NaN   \n",
              "9                        NaN                       NaN   \n",
              "10                       NaN                       NaN   \n",
              "11                       NaN                       NaN   \n",
              "12                       NaN                       NaN   \n",
              "13                       NaN                       NaN   \n",
              "14                         5                         5   \n",
              "15                         5                         0   \n",
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              "22                         5                         0   \n",
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              "24                       NA                        NaN   \n",
              "25                       NA                        NaN   \n",
              "26                       NaN                       NA    \n",
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              "10                         NaN                         NaN   \n",
              "11                         NaN                         NaN   \n",
              "12                         NaN                         NaN   \n",
              "13                         NaN                         NaN   \n",
              "14                           0                           0   \n",
              "15                           5                           5   \n",
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              "18                           0                           0   \n",
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              "24                           5                           5   \n",
              "25                           5                           5   \n",
              "26                         NaN                         NA    \n",
              "27                         NaN                         NA    \n",
              "\n",
              "   Status Quo of power relations (before process)  \\\n",
              "0                                             NaN   \n",
              "1                                             NaN   \n",
              "2                                             NaN   \n",
              "3                                             NaN   \n",
              "4                                             NaN   \n",
              "5                                             NaN   \n",
              "6                                             NaN   \n",
              "7                                             NaN   \n",
              "8                                             NaN   \n",
              "9                                             NaN   \n",
              "10                                            NaN   \n",
              "11                                            NaN   \n",
              "12                                            NaN   \n",
              "13                                            NaN   \n",
              "14                                            NA    \n",
              "15                                            NaN   \n",
              "16                                            NA    \n",
              "17                                            NaN   \n",
              "18                                            NaN   \n",
              "19                                            NA    \n",
              "20                                            NA    \n",
              "21                                            NaN   \n",
              "22                                            NaN   \n",
              "23                                            NA    \n",
              "24                                              0   \n",
              "25                                            NaN   \n",
              "26                                            NaN   \n",
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              "\n",
              "   Status Quo of power relations (after process)   Actor 3  Actor 4  Actor 5  \n",
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              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 6</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 7</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 8</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NA</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Actor 8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-77c2fd11-6a38-4742-9404-67c3dd3a3a52')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-77c2fd11-6a38-4742-9404-67c3dd3a3a52 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-77c2fd11-6a38-4742-9404-67c3dd3a3a52');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-7eb7d648-51c5-42ef-9303-fd449bfe19fe\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7eb7d648-51c5-42ef-9303-fd449bfe19fe')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-7eb7d648-51c5-42ef-9303-fd449bfe19fe button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_9cffa5ab-19ba-4ba1-bd9c-01ea39b08f4c\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('first_order_df_not_matching_sedrun')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9cffa5ab-19ba-4ba1-bd9c-01ea39b08f4c button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('first_order_df_not_matching_sedrun');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "first_order_df_not_matching_sedrun",
              "summary": "{\n  \"name\": \"first_order_df_not_matching_sedrun\",\n  \"rows\": 28,\n  \"fields\": [\n    {\n      \"column\": \"Actor 1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Actor 2\",\n          \"Actor 3\",\n          \"Actor 7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Biodiversity (before process)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.0,\n          1,\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Biodiversity (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5.0,\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Landscape (before process)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Landscape (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"NA \",\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Agriculture (before process)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Agriculture (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hydropower (before process)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"NA \",\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hydropower (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5,\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Energy security (before process)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Energy security (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"NA \",\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Finances (before process)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Finances (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"NA \",\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Employment (before process)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Employment (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"NA \",\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Status Quo of power relations (before process)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Status Quo of power relations (after process) \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          \"NA \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actor 3\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Actor 1\",\n          \"Actor 2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actor 4\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Actor 1\",\n          \"Actor 2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actor 5\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Actor 1\",\n          \"Actor 2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Figure 2, quantitative barcharts with number of trade-offs and synergies\n",
        "\n",
        "def calculate_tradeoffs_and_synergies(df):\n",
        "    tradeoffs = (df == 1).sum(axis=1)\n",
        "    synergies = (df == 0).sum(axis=1)\n",
        "    return tradeoffs, synergies\n",
        "\n",
        "def percentage_change(before, after):\n",
        "    if before == 0:\n",
        "        return np.nan\n",
        "    return ((after - before) / before) * 100\n",
        "\n",
        "def format_percentage_change(change):\n",
        "    if np.isnan(change): return \"N/A\"\n",
        "    return f\"{'+' if change > 0 else ''}{change:.0f}%\" if change != 0 else \"0%\"\n",
        "\n",
        "def process_data(df, label=\"\"):\n",
        "    list_before = [col for col in df.columns if \"before process\" in col]\n",
        "    list_after = [col for col in df.columns if \"after process\" in col]\n",
        "    before = df[list_before] if list_before else pd.DataFrame()\n",
        "    after = df[list_after] if list_after else pd.DataFrame()\n",
        "\n",
        "    if not before.empty:\n",
        "        tradeoffs_before, synergies_before = calculate_tradeoffs_and_synergies(before)\n",
        "    else:\n",
        "        tradeoffs_before = synergies_before = pd.Series(dtype=int)\n",
        "\n",
        "    if not after.empty:\n",
        "        tradeoffs_after, synergies_after = calculate_tradeoffs_and_synergies(after)\n",
        "    else:\n",
        "        tradeoffs_after = synergies_after = pd.Series(dtype=int)\n",
        "\n",
        "    synergies_vals = [synergies_before.sum(), synergies_after.sum()]\n",
        "    tradeoffs_vals = [tradeoffs_before.sum(), tradeoffs_after.sum()]\n",
        "    pct_synergies = percentage_change(*synergies_vals)\n",
        "    pct_tradeoffs = percentage_change(*tradeoffs_vals)\n",
        "\n",
        "    return synergies_vals, tradeoffs_vals, pct_synergies, pct_tradeoffs\n",
        "\n",
        "def plot_synergy_tradeoff(ax, synergies, tradeoffs, pct_synergies, pct_tradeoffs, colors, label=\"\"):\n",
        "    positions = [0, 0.45, 1.25, 1.7]\n",
        "    values = [synergies[0], synergies[1], tradeoffs[0], tradeoffs[1]]\n",
        "\n",
        "    bars = ax.bar(positions, values, color=colors, width=0.35)\n",
        "\n",
        "    for bar in bars:\n",
        "        y = bar.get_height()\n",
        "        ax.text(bar.get_x() + bar.get_width() / 2, y + 1, f'{y:.0f}', ha='center', va='bottom', fontsize=14)\n",
        "\n",
        "    ax.set_ylabel('Number of synergies/trade-offs', fontsize=14)\n",
        "    ax.set_xticks([0.225, 1.475])\n",
        "    ax.set_xticklabels(['', ''])\n",
        "    ax.set_ylim(0, 25)\n",
        "    ax.text(0, -1.5, 'Before', ha='center', fontsize=14)\n",
        "    ax.text(0.45, -1.5, 'After', ha='center', fontsize=14)\n",
        "    ax.text(1.25, -1.5, 'Before', ha='center', fontsize=14)\n",
        "    ax.text(1.7, -1.5, 'After', ha='center', fontsize=14)\n",
        "\n",
        "    ax.annotate(format_percentage_change(pct_synergies), xy=(0.19, 0.81), xycoords='axes fraction', fontsize=14)\n",
        "    ax.annotate(format_percentage_change(pct_tradeoffs), xy=(0.70, 0.81), xycoords='axes fraction', fontsize=14)\n",
        "\n",
        "    # Add subplot label (A, B, C, D)\n",
        "    ax.text(-0.1, 1.02, label, transform=ax.transAxes, fontsize=16, fontweight='bold', va='top', ha='right')\n",
        "\n",
        "    # Add legend once per subplot\n",
        "    ax.legend(handles=[\n",
        "        Patch(color=\"#1f77b4\", label=\"Synergies\"),\n",
        "        Patch(color=\"#d62728\", label=\"Trade-offs\")\n",
        "    ], loc='upper left', fontsize=12)\n",
        "\n",
        "# --- Colors ---\n",
        "colors = [\"#1f77b4\", \"#1f77b4\", \"#d62728\", \"#d62728\"]\n",
        "\n",
        "# --- Data Processing ---\n",
        "\n",
        "# First-order\n",
        "s1, t1, pct_s1, pct_t1 = process_data(savognin_df, \"Savognin\")\n",
        "s2, t2, pct_s2, pct_t2 = process_data(sedrun_df, \"Sedrun\")\n",
        "\n",
        "# Second-order\n",
        "s3, t3, pct_s3, pct_t3 = process_data(first_order_df_not_matching_savognin, \"Savognin\")\n",
        "s4, t4, pct_s4, pct_t4 = process_data(first_order_df_not_matching_sedrun, \"Sedrun\")\n",
        "\n",
        "\n",
        "# --- Plotting ---\n",
        "fig, axs = plt.subplots(2, 2, figsize=(16, 12))\n",
        "\n",
        "plot_synergy_tradeoff(axs[0, 0], s1, t1, pct_s1, pct_t1, colors, label=\"A\")\n",
        "plot_synergy_tradeoff(axs[0, 1], s2, t2, pct_s2, pct_t2, colors, label=\"B\")\n",
        "plot_synergy_tradeoff(axs[1, 0], s3, t3, pct_s3, pct_t3, colors, label=\"C\")\n",
        "plot_synergy_tradeoff(axs[1, 1], s4, t4, pct_s4, pct_t4, colors, label=\"D\")\n",
        "\n",
        "# Adjust layout to leave space\n",
        "plt.tight_layout(rect=[0, 0.08, 1, 1])\n",
        "\n",
        "# Subplot identifier legend\n",
        "fig.text(0.5, 0.04,\n",
        "         \"(A) Savognin First Order    (B) Sedrun First Order   (C) Savognin Second Order    (D) Sedrun Second Order\",\n",
        "         ha='center', fontsize=14)\n",
        "\n",
        "plt.savefig(\"Perceptions_Multipanelfigure3.pdf\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 816
        },
        "id": "-ahblBhQslXj",
        "outputId": "2af28fa2-da8c-4803-fb60-7f25b3bf9648"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1200 with 4 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Figure 1, Network analysis First order perceptions\n",
        "\n",
        "# --- Helper functions ---\n",
        "def clean_topic_names(topic_names):\n",
        "    return [name.replace(\" (before process)\", \"\").replace(\" (after process)\", \"\") for name in topic_names]\n",
        "\n",
        "def create_network(incidence_matrix, actor_names, topic_names, ax):\n",
        "    n_actors = len(actor_names)\n",
        "    n_topics = len(topic_names)\n",
        "    cleaned_topic_names = clean_topic_names(topic_names)\n",
        "\n",
        "    g = ig.Graph(directed=False)\n",
        "    g.add_vertices(n_actors + n_topics)\n",
        "    g.vs[\"name\"] = list(actor_names) + cleaned_topic_names\n",
        "\n",
        "    for i in range(n_actors):\n",
        "        for j in range(n_topics):\n",
        "            val = incidence_matrix[i, j]\n",
        "            if val in [0, 1]:\n",
        "                g.add_edge(i, n_actors + j)\n",
        "\n",
        "    # Edge colors\n",
        "    edge_colors = []\n",
        "    for i in range(n_actors):\n",
        "        for j in range(n_topics):\n",
        "            val = incidence_matrix[i, j]\n",
        "            if val == 1:\n",
        "                edge_colors.append('#d62728')  # Conflict\n",
        "            elif val == 0:\n",
        "                edge_colors.append('#1f77b4')  # Synergy\n",
        "\n",
        "    # Smaller node sizes for clarity\n",
        "    actor_size = 40  # or 50 if you prefer slightly bigger actors\n",
        "    topic_tradeoffs = np.sum(incidence_matrix == 1, axis=0)\n",
        "\n",
        "    # Apply logarithmic scaling to topic trade-offs\n",
        "    with np.errstate(divide='ignore'):\n",
        "        log_tradeoffs = np.log1p(topic_tradeoffs)  # log1p avoids log(0)\n",
        "\n",
        "# Normalize the log values\n",
        "    if np.max(log_tradeoffs) > 0:\n",
        "       normalized_log_tradeoffs = log_tradeoffs / np.max(log_tradeoffs)\n",
        "    else:\n",
        "        normalized_log_tradeoffs = log_tradeoffs  # avoid divide-by-zero\n",
        "\n",
        "# Scale topic sizes with lower boost range to reduce dominance\n",
        "    topic_sizes = 30 + normalized_log_tradeoffs * 40  # Base size + scaled log boost\n",
        "\n",
        "    node_sizes = [actor_size] * n_actors + topic_sizes.tolist()\n",
        "\n",
        "    node_colors = ['lavender'] * n_actors + ['lightgreen'] * n_topics\n",
        "\n",
        "    # Use Kamada-Kawai layout for good spacing\n",
        "    layout = g.layout_kamada_kawai()\n",
        "    layout_coords = np.array(layout.coords)\n",
        "\n",
        "    # Add small jitter to spread overlapping nodes\n",
        "    layout_coords += np.random.normal(scale=5, size=layout_coords.shape)\n",
        "\n",
        "    # Compute scale factor based on node sizes to avoid overlap\n",
        "    max_node_size = max(node_sizes)\n",
        "    min_spacing = max_node_size * 1.5  # minimum spacing between nodes\n",
        "\n",
        "    # Scale up layout so that nodes are spaced proportionally to their size\n",
        "    layout_coords *= min_spacing\n",
        "\n",
        "    # Center layout inside 1000x1000 plot space\n",
        "    x_coords, y_coords = layout_coords[:, 0], layout_coords[:, 1]\n",
        "    x_min, x_max = x_coords.min(), x_coords.max()\n",
        "    y_min, y_max = y_coords.min(), y_coords.max()\n",
        "\n",
        "    # Compute range and apply normalization with margin\n",
        "    margin = 50  # pixels of margin around network\n",
        "    x_range = x_max - x_min\n",
        "    y_range = y_max - y_min\n",
        "\n",
        "    layout_coords[:, 0] = ((x_coords - x_min) / x_range) * (1000 - 2 * margin) + margin\n",
        "    layout_coords[:, 1] = ((y_coords - y_min) / y_range) * (1000 - 2 * margin) + margin\n",
        "\n",
        "\n",
        "    # Separate actor and topic node coordinates\n",
        "    topic_coords = layout_coords[n_actors:]\n",
        "    actor_coords = layout_coords[:n_actors]\n",
        "\n",
        "    # Define a minimum distance between topic nodes\n",
        "    min_distance = 80  # Adjust as needed\n",
        "\n",
        "    # Compute pairwise distances between topic nodes\n",
        "    dist_matrix = squareform(pdist(topic_coords))\n",
        "\n",
        "    # Slightly adjust positions to enforce spacing\n",
        "    for i in range(len(topic_coords)):\n",
        "        for j in range(i + 1, len(topic_coords)):\n",
        "            distance = dist_matrix[i, j]\n",
        "            if distance < min_distance and distance > 0:\n",
        "                # Push nodes away from each other proportionally\n",
        "                direction = topic_coords[i] - topic_coords[j]\n",
        "                norm = np.linalg.norm(direction)\n",
        "                if norm == 0:\n",
        "                    direction = np.random.normal(size=2)\n",
        "                    norm = np.linalg.norm(direction)\n",
        "                direction /= norm\n",
        "                shift = (min_distance - distance) / 2 * direction\n",
        "                topic_coords[i] += shift\n",
        "                topic_coords[j] -= shift\n",
        "\n",
        "    # Recombine updated layout\n",
        "    layout_coords = np.vstack([actor_coords, topic_coords])\n",
        "\n",
        "\n",
        "    # Optional: Further scale the layout by a factor to make the network bigger\n",
        "    scale_factor = 1.3  # Increase this to further enlarge the network (up to 30% more)\n",
        "    layout_coords *= scale_factor\n",
        "\n",
        "    # Plot the graph with dynamic space use\n",
        "    ax.set_aspect(\"equal\")\n",
        "    ax.axis(\"off\")\n",
        "\n",
        "    ig.plot(\n",
        "        g,\n",
        "        layout=layout_coords.tolist(),\n",
        "        target=ax,\n",
        "        vertex_size=node_sizes,\n",
        "        vertex_color=node_colors,\n",
        "        edge_color=edge_colors,\n",
        "        edge_width=2,\n",
        "        bbox=(0, 0, 1000, 1000),  # Matches normalized layout\n",
        "        margin=0\n",
        "    )\n",
        "\n",
        "    # Add labels\n",
        "    for i, v in enumerate(g.vs):\n",
        "        ax.text(layout_coords[i][0], layout_coords[i][1], v[\"name\"], fontsize=13,\n",
        "                ha='center', va='center')\n",
        "\n",
        "# --- Plot Setup ---\n",
        "fig, axs = plt.subplots(2, 2, figsize=(20, 20))\n",
        "\n",
        "# Each panel data: (df, actor list)\n",
        "data = [\n",
        "    (savognin_before, savognin_df.index),  # (a)\n",
        "    (sedrun_before, sedrun_df.index),     # (b)\n",
        "    (savognin_after, savognin_df.index),  # (c)\n",
        "    (sedrun_after, sedrun_df.index)       # (d)\n",
        "]\n",
        "\n",
        "labels = ['(a)', '(b)', '(c)', '(d)']\n",
        "label_positions = {\n",
        "    'A': (0.06, 0.95),\n",
        "    'B': (0.54, 0.95),\n",
        "    'C': (0.06, 0.48),\n",
        "    'D': (0.54, 0.48)\n",
        "}\n",
        "\n",
        "# --- Draw each subplot ---\n",
        "for ax, (df_part, actor_index), label in zip(axs.flat, data, labels):\n",
        "    if df_part is not None:\n",
        "        create_network(df_part.to_numpy(), list(actor_index), df_part.columns, ax)\n",
        "    else:\n",
        "        ax.text(0.5, 0.5, \"No data\", ha='center', fontsize=14)\n",
        "        ax.axis(\"off\")\n",
        "\n",
        "# Add panel labels\n",
        "for label, (x, y) in label_positions.items():\n",
        "    fig.text(x, y, label, fontsize=14, fontweight='bold')\n",
        "\n",
        "# --- Combined Legend ---\n",
        "legend_elements = [\n",
        "    Line2D([0], [0], color='#d62728', lw=5, label='Trade-off'),\n",
        "    Line2D([0], [0], color='#1f77b4', lw=5, label='Synergy'),\n",
        "    Line2D([0], [0], marker='o', color='w', label='Actor', markerfacecolor='lavender', markersize=15),\n",
        "    Line2D([0], [0], marker='o', color='w', label='Topic', markerfacecolor='lightgreen', markersize=15),\n",
        "    Line2D([0], [0], color='none', label=''),\n",
        "    Line2D([0], [0], color='none', label='(A) Savognin – Before Process'),\n",
        "    Line2D([0], [0], color='none', label='(B) Sedrun – Before Process'),\n",
        "    Line2D([0], [0], color='none', label='(C) Savognin – After Process'),\n",
        "    Line2D([0], [0], color='none', label='(D) Sedrun – After Process'),\n",
        "]\n",
        "\n",
        "# --- Layout Adjustments ---\n",
        "plt.subplots_adjust(hspace=0.2, wspace=0.2, bottom=0.1, top=0.95)\n",
        "fig.legend(handles=legend_elements, loc='lower center', ncol=2, frameon=False, fontsize=12)\n",
        "\n",
        "plt.savefig(\"Network_analysis_V5.pdf\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "x1ECiBUMnHko",
        "outputId": "bffb3506-6751-4576-e2c8-5d8f7e95abe1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'plt' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-2083269589>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[0;31m# --- Plot Setup ---\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m \u001b[0;31m# Each panel data: (df, actor list)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
          ]
        }
      ]
    }
  ]
}